A
case study for text breaking analysis and text-mining technology to identify
important data themes
case study for text breaking analysis and text-mining technology to identify
important data themes
How social media (Facebook, Twitter, Instagram) has used text analysis and text-mining technology to identify important data themes.
Summery
Text mining has become one of the most interesting fields
that have been integrated into several research areas, such as computer
logistics, information gathering (IR) and data mining. Natural language
processing techniques (NLP) have been used to extract knowledge from text
written by humans. Text mining reads an unstructured form of data to provide
meaningful information models as quickly as possible. Social networking is an
excellent source of communication because most people around the world use
these websites in their daily lives to keep in touch with each other. It is
common to not write a sentence with the right grammar and spelling. This
exercise can generate various ambiguities such as dictionary, syntax and
semantics. Because of this kind of unclear data, it is difficult to know the
real order of data. As a result, we do a survey to find different methods for
extracting text to get different text orders on social media. The purpose of
this study is to describe how social media has used text analysis and
text-mining technology to identify important data themes. This study focused on
analysis of text-mining studies related to Face-book and Twitter; the two
dominant social media in the world. The results of this study can serve as a
basis for future research into text breaking.
that have been integrated into several research areas, such as computer
logistics, information gathering (IR) and data mining. Natural language
processing techniques (NLP) have been used to extract knowledge from text
written by humans. Text mining reads an unstructured form of data to provide
meaningful information models as quickly as possible. Social networking is an
excellent source of communication because most people around the world use
these websites in their daily lives to keep in touch with each other. It is
common to not write a sentence with the right grammar and spelling. This
exercise can generate various ambiguities such as dictionary, syntax and
semantics. Because of this kind of unclear data, it is difficult to know the
real order of data. As a result, we do a survey to find different methods for
extracting text to get different text orders on social media. The purpose of
this study is to describe how social media has used text analysis and
text-mining technology to identify important data themes. This study focused on
analysis of text-mining studies related to Face-book and Twitter; the two
dominant social media in the world. The results of this study can serve as a
basis for future research into text breaking.
Keywords- Social media, Social
network, Analysis, Text Mining,
Sentiment Analysis, Open Source, Twitter Data Analysis, Social Data Mining,
network, Analysis, Text Mining,
Sentiment Analysis, Open Source, Twitter Data Analysis, Social Data Mining,
Introduction
As we know there are different social networks,
Facebook and Twitter are considered the most congested. These network sites
have facilitated communication with friends and family members without much
effort. People with different values ​​come together by sharing their ideas,
interests and knowledge. Nowadays, it is very easy for anyone to meet interesting
people to learn and share valuable information. Technology advances have shrunk
the world. The distances come closer and information exchange is easier.
Through these social networks, people can easily and securely transmit their
views on various global problems by retrieving their messages, comments and
blogs.
Facebook and Twitter are considered the most congested. These network sites
have facilitated communication with friends and family members without much
effort. People with different values ​​come together by sharing their ideas,
interests and knowledge. Nowadays, it is very easy for anyone to meet interesting
people to learn and share valuable information. Technology advances have shrunk
the world. The distances come closer and information exchange is easier.
Through these social networks, people can easily and securely transmit their
views on various global problems by retrieving their messages, comments and
blogs.
A study said that social media, including Google Apps, makes it easier
for people to learn, collaborate, and share ideas with each other. In addition,
social media has been integrated into many forms of learning, such as e-learning
and distance learning. Regardless of the scenario, people do not think about
using properly structured sentences, grammar and spelling. It doesn’t matter if
they search the site, comment on or join people through different discussion
forums. People use irregular data patterns to convey their messages. It seems
that they run without time, but it is not easy to produce accurate and
consistent data models due to the use of this unstructured language. On various
social networks, the most common method of interaction is to use text. People
share their knowledge and information through blogs, messages and discussions
by writing in their own language. The basic use of text mining methods is to
clarify the text so that everyone can easily write or search the most
appropriate way.
As people write words
or sentences with errors, to let them write or search with correct grammar and
structured sentences, textual research is used. Text mining involves extracting
unknown data from everyone. If we compare web search to text mining, the two
conditions are very different from each other. If we are talking about web
search, you know exactly what to look for. But when it comes to text mining,
the main goal is to get the most appropriate information based on the written
text, whether structured or not. This technology only requires a certain
alphabet to extract data that is then converted into different suggestions and
expectations. Text mining seems to include all the automatic language
processing. For example, exploring link structures, references in academic
writing, and hyperlinks in a web form are important sources of data that fall
outside the conventional domain of NLP. NLP is one of the hot topics that deal
with the link between the huge amount of unstructured texts on social media, in
addition to the analysis and interpretation of human languages. Several
research papers were collected in different databases for analysis and use in
this study.
or sentences with errors, to let them write or search with correct grammar and
structured sentences, textual research is used. Text mining involves extracting
unknown data from everyone. If we compare web search to text mining, the two
conditions are very different from each other. If we are talking about web
search, you know exactly what to look for. But when it comes to text mining,
the main goal is to get the most appropriate information based on the written
text, whether structured or not. This technology only requires a certain
alphabet to extract data that is then converted into different suggestions and
expectations. Text mining seems to include all the automatic language
processing. For example, exploring link structures, references in academic
writing, and hyperlinks in a web form are important sources of data that fall
outside the conventional domain of NLP. NLP is one of the hot topics that deal
with the link between the huge amount of unstructured texts on social media, in
addition to the analysis and interpretation of human languages. Several
research papers were collected in different databases for analysis and use in
this study.
The search terms include “Text search with social media”,
“Text search with Face-book” and “Text search with
Twitter”.
“Text search with Face-book” and “Text search with
Twitter”.
This survey is categorized as follows:
Section II provides a
complete overview of the text jump field.
complete overview of the text jump field.
Section III presents other related
studies.
studies.
Findings and perspectives for the future are presented in section IV.
Companies have identified data-driven strategies as
the ideal plan for growth. It is easier to understand this theory. After all,
would not it be beneficial for a company to get an idea of ​​the perception of
its products on the market without having to consult individual opinions of
everyone? Would not it be better if they could determine which political
candidate is ideal for their public image without having to analyze them all
individually? As a result, market research and research are among the most
heavily invested areas in the world today.
the ideal plan for growth. It is easier to understand this theory. After all,
would not it be beneficial for a company to get an idea of ​​the perception of
its products on the market without having to consult individual opinions of
everyone? Would not it be better if they could determine which political
candidate is ideal for their public image without having to analyze them all
individually? As a result, market research and research are among the most
heavily invested areas in the world today.
Social networking sites such as
Twitter and Face-book are ideal for this purpose. Messages or messages shared
by people on these platforms with their friends are freely available or kept
confidential. They give companies the opportunity to get public opinion on
topics they want to share with a large number of people. The processing of
public inquiries and impressions using specially designed computing systems is
a common goal for interconnected areas such as subjectivity analysis, opinion
formation and sentiment analysis. The creation of problem-solving methods or
methods for defining structure and preferences or summarizing perceived
messages on specific topics, opportunities or products is another objective of
the study. For example, these methods can be used to measure support for
special occasions or objects, or to determine voices up or down for specific
movies based on their criticism.
Twitter and Face-book are ideal for this purpose. Messages or messages shared
by people on these platforms with their friends are freely available or kept
confidential. They give companies the opportunity to get public opinion on
topics they want to share with a large number of people. The processing of
public inquiries and impressions using specially designed computing systems is
a common goal for interconnected areas such as subjectivity analysis, opinion
formation and sentiment analysis. The creation of problem-solving methods or
methods for defining structure and preferences or summarizing perceived
messages on specific topics, opportunities or products is another objective of
the study. For example, these methods can be used to measure support for
special occasions or objects, or to determine voices up or down for specific
movies based on their criticism.
Text mining makes it easy to get meaningful and
structured data from irregular data models, and it is certainly not an easy
task for computers to understand unstructured data and structure them. People
can do this task without extra effort because of the availability of different
language techniques. However, human speed and space are limited compared to
computers. In other words, computers are much better than people to perform
these tasks. Most of the existing data in an organization is represented in a
text format. Therefore, if we compare data extraction with text mining, text extraction
is more important. However, since text breaking is used to structure
unstructured text data, this task is more demanding than data mining.
Generally, data on social media is not collected for research purposes, so it
is mandatory to change the structure of social media. 80% of the text available
on the web is unstructured, while only 20% is structured.
structured data from irregular data models, and it is certainly not an easy
task for computers to understand unstructured data and structure them. People
can do this task without extra effort because of the availability of different
language techniques. However, human speed and space are limited compared to
computers. In other words, computers are much better than people to perform
these tasks. Most of the existing data in an organization is represented in a
text format. Therefore, if we compare data extraction with text mining, text extraction
is more important. However, since text breaking is used to structure
unstructured text data, this task is more demanding than data mining.
Generally, data on social media is not collected for research purposes, so it
is mandatory to change the structure of social media. 80% of the text available
on the web is unstructured, while only 20% is structured.
Text
mining and data mining
mining and data mining
When it comes to publishing comments on publications
on different social networking sites, no simple structured technology is
available that causes problems when directly using data. Data available in text
format is much more important and that is why text mining generates very
commercial value. A study indicates that data mining represents derivatives of
a pattern or significant principles from a spatial database to determine a
particular problem or problem. Data mining differs from text mining. A recent
study showed that text breaking is much more complex than data mining because
it contains irregular and unstructured data models, while data mining is about
structured data sets. The tools used in data extraction were only about
structured data. Text mining is like an intelligence system that extracts
appropriate words or phrases from the wrong word and then makes them special
suggestions. Text mining is basically a new field with the main purpose of data
collection, machine learning, and information mining and computer science.
on different social networking sites, no simple structured technology is
available that causes problems when directly using data. Data available in text
format is much more important and that is why text mining generates very
commercial value. A study indicates that data mining represents derivatives of
a pattern or significant principles from a spatial database to determine a
particular problem or problem. Data mining differs from text mining. A recent
study showed that text breaking is much more complex than data mining because
it contains irregular and unstructured data models, while data mining is about
structured data sets. The tools used in data extraction were only about
structured data. Text mining is like an intelligence system that extracts
appropriate words or phrases from the wrong word and then makes them special
suggestions. Text mining is basically a new field with the main purpose of data
collection, machine learning, and information mining and computer science.
Text
search in social networks
search in social networks
The importance of text breaks has been increased due
to the important contributions in the field of technology. The data memory
reported is also important, but due to the progress made, text mining occurs.
It is indeed a great effort to convey valuable information and knowledge
through powerful processes of treatment and recovery from the irregular
information. At that time, structured data became less important and unstructured
data became more popular. Most organizations turn to text extraction and forget
about the concept of data extraction. Researchers reported that all social
networking sites provide a good space for individuals to facilitate interaction
and share their views and opinions. The best thing about these websites is that
it has become easy for people to understand a particular person based on their
activities. Through all these activities, people with different customs and
values ​​have come together through a better understanding of their feelings,
perceptions and interests. At this time, the user interfaces will be equipped
with personality-based features. Custom designs have been used in e-commerce, e-learning
and information filtering to enhance different styles and skills.
to the important contributions in the field of technology. The data memory
reported is also important, but due to the progress made, text mining occurs.
It is indeed a great effort to convey valuable information and knowledge
through powerful processes of treatment and recovery from the irregular
information. At that time, structured data became less important and unstructured
data became more popular. Most organizations turn to text extraction and forget
about the concept of data extraction. Researchers reported that all social
networking sites provide a good space for individuals to facilitate interaction
and share their views and opinions. The best thing about these websites is that
it has become easy for people to understand a particular person based on their
activities. Through all these activities, people with different customs and
values ​​have come together through a better understanding of their feelings,
perceptions and interests. At this time, the user interfaces will be equipped
with personality-based features. Custom designs have been used in e-commerce, e-learning
and information filtering to enhance different styles and skills.
Text
mining efforts to solve various NLP problems
mining efforts to solve various NLP problems
A study of NLP hard indicates that text memory is
responsible for structuring irregular data models written in human language.
Since most people interact with each other as text, breaking text is the best
technique to use for people who cannot share structured data. Among other
things, NLP is considered the most amazing field of research. The main
objective of NLP is to seek information on how computer systems analyze and
receive information in people’s languages ​​in order to create high quality
applications. The art of sharing meaningful information using unusual and
meaningless data is really a good thing. The text mining technique described by
examines the content to extract meaningful data that can be used for a
particular purpose. It would seem that text memory should include the overall
system of NLP in its system to effectively examine human language and structure
unstructured data models accordingly. As technology advances day by day, the
text extraction system is getting better and better and that’s what everyone is
looking for.
responsible for structuring irregular data models written in human language.
Since most people interact with each other as text, breaking text is the best
technique to use for people who cannot share structured data. Among other
things, NLP is considered the most amazing field of research. The main
objective of NLP is to seek information on how computer systems analyze and
receive information in people’s languages ​​in order to create high quality
applications. The art of sharing meaningful information using unusual and
meaningless data is really a good thing. The text mining technique described by
examines the content to extract meaningful data that can be used for a
particular purpose. It would seem that text memory should include the overall
system of NLP in its system to effectively examine human language and structure
unstructured data models accordingly. As technology advances day by day, the
text extraction system is getting better and better and that’s what everyone is
looking for.
Text
search on Face-book
search on Face-book
Social networks are growing at a rapid pace without
interruption. Most importantly, unstructured data is stored on these networks
as it is a large pool and this data is relevant to a variety of areas,
including government, business and health. Data mining techniques tend to
transform unstructured data into a systematic arrangement. Facebook is today
one of the most popular social media. Many people around the world use this
medium to express their thoughts, thoughts, sorrows, pleasures and poems. The
researchers selected a number of Facebook variables that could create the right
situation to carry out our investigations. The valuable user perspective
statistics are provided by Facebook’s profiles and activities, which expose the
real objects instead of the projected or idealized character. Digital data has
grown tremendously. The most important area for professionals is now data
extraction and knowledge discovery. In addition, there was a strong need to
make this information useful information and information. A number of
applications such as company management and market analysis have utilized
information and knowledge from large-scale data. The information is stored as
text in different applications. Text mining is one of the latest research
areas. The biggest problem is extracting the information that the user needs.
interruption. Most importantly, unstructured data is stored on these networks
as it is a large pool and this data is relevant to a variety of areas,
including government, business and health. Data mining techniques tend to
transform unstructured data into a systematic arrangement. Facebook is today
one of the most popular social media. Many people around the world use this
medium to express their thoughts, thoughts, sorrows, pleasures and poems. The
researchers selected a number of Facebook variables that could create the right
situation to carry out our investigations. The valuable user perspective
statistics are provided by Facebook’s profiles and activities, which expose the
real objects instead of the projected or idealized character. Digital data has
grown tremendously. The most important area for professionals is now data
extraction and knowledge discovery. In addition, there was a strong need to
make this information useful information and information. A number of
applications such as company management and market analysis have utilized
information and knowledge from large-scale data. The information is stored as
text in different applications. Text mining is one of the latest research
areas. The biggest problem is extracting the information that the user needs.
The knowledge discovery process involves an important step which is considered
to be exploration of texts. Hidden information is extracted from unstructured
to semi-structured data during this process. Retrieving information from a
number of written resources and their automatic detection is called text
mining. In addition, computers are also used for the purpose and to achieve
this goal. The researchers illustrated techniques, methods and challenges of
text mining. These successful techniques would be described to provide ease of
use with respect to acquiring information during text extraction. The study
examined situations in which each technique could be beneficial for another
number of users. A number of commercial organizations would be reviewed based on
the mining data their employees displayed on LinkedIn, Facebook and other open
sources. A network of informal social relations between employees is extracted
through web brokers developed for this purpose. Depending on the results,
leadership roles can be identified within the organization and this can be
achieved absolutely using machine learning techniques in addition to the
centrality analysis. Clustering a company’s social network and gathering
information available within each group can give rise to valuable non-trivial
perceptions. Knowledge of the informal relationship network is an important
asset or threat to the lead organization. In addition to analyzing
organizations’ social networks, algorithms and methods are used to collect data
from freely available sources.
to be exploration of texts. Hidden information is extracted from unstructured
to semi-structured data during this process. Retrieving information from a
number of written resources and their automatic detection is called text
mining. In addition, computers are also used for the purpose and to achieve
this goal. The researchers illustrated techniques, methods and challenges of
text mining. These successful techniques would be described to provide ease of
use with respect to acquiring information during text extraction. The study
examined situations in which each technique could be beneficial for another
number of users. A number of commercial organizations would be reviewed based on
the mining data their employees displayed on LinkedIn, Facebook and other open
sources. A network of informal social relations between employees is extracted
through web brokers developed for this purpose. Depending on the results,
leadership roles can be identified within the organization and this can be
achieved absolutely using machine learning techniques in addition to the
centrality analysis. Clustering a company’s social network and gathering
information available within each group can give rise to valuable non-trivial
perceptions. Knowledge of the informal relationship network is an important
asset or threat to the lead organization. In addition to analyzing
organizations’ social networks, algorithms and methods are used to collect data
from freely available sources.
A robot Web was developed to obtain employee
profiles from six targeted organizations through data collection on Facebook. A
social network topology has been created for each organization. Machine
learning algorithms and centrality actions have been implemented to detect
hidden management positions within each company. In addition, the algorithms
revealed the social clusters in these organizations, which allowed us to
understand the communication network of each company in addition to the
organizational structure.
profiles from six targeted organizations through data collection on Facebook. A
social network topology has been created for each organization. Machine
learning algorithms and centrality actions have been implemented to detect
hidden management positions within each company. In addition, the algorithms
revealed the social clusters in these organizations, which allowed us to
understand the communication network of each company in addition to the
organizational structure.
According to a study, it has become clear that
social media data will simply be abused. The schedule contains a structured
approach and its application. In addition, it is about performing a statistical
analysis of clusters in addition to a comprehensive analysis of social media,
so that researchers can determine the relationships between the key factors.
Qualitative social media data can be quantified by these systems and then
grouped according to their similar properties and then used as decision support
tools. The SAMSUNG Mobile Facebook page, where Samsung’s smartphones were
introduced, was used for the data collection process. The comment from Facebook
users on the subtitle Facebook page is called “data”. In 3 months, approximately
128371 comments have been downloaded. Only comments in English have been
analyzed. Then the conceptual analysis for conceptual analysis was used and
finally a statistical group analysis was performed by performing a relational
analysis. As a result, social media data is integrated by applying statistical
group analysis and performed based on the outcome of the conceptual analysis.
Researchers therefore have the opportunity to classify a large set of data into
several subgroups, sometimes called objects. One of its areas of application is
marketing. Factors that can be managed in some cases are also minimized by
these types of techniques.
social media data will simply be abused. The schedule contains a structured
approach and its application. In addition, it is about performing a statistical
analysis of clusters in addition to a comprehensive analysis of social media,
so that researchers can determine the relationships between the key factors.
Qualitative social media data can be quantified by these systems and then
grouped according to their similar properties and then used as decision support
tools. The SAMSUNG Mobile Facebook page, where Samsung’s smartphones were
introduced, was used for the data collection process. The comment from Facebook
users on the subtitle Facebook page is called “data”. In 3 months, approximately
128371 comments have been downloaded. Only comments in English have been
analyzed. Then the conceptual analysis for conceptual analysis was used and
finally a statistical group analysis was performed by performing a relational
analysis. As a result, social media data is integrated by applying statistical
group analysis and performed based on the outcome of the conceptual analysis.
Researchers therefore have the opportunity to classify a large set of data into
several subgroups, sometimes called objects. One of its areas of application is
marketing. Factors that can be managed in some cases are also minimized by
these types of techniques.
A study of examined
social data as a systematic data mining architecture, results showed that Facebook
as the social network is the most important source of data. In addition to this
approach, the author has added information about “my wall”, articles
about me, my age and Facebook comments. It was taken as a raw data, which is
then applied to study and monitor the tactics of analysis. The study also
looked at images for advertising their products and for the decision-making
process. A number of data mining techniques predict the constraint of
intellectual knowledge from social data. It essentially organizes essential
facts and other applied activities allowing users to be in touch with their
colleagues on social networks (Facebook). For recovery from the Facebook user
database, use the Facebook API Secret Application key and the Facebook API
Facebook API key. As a result, WEKA files and mining techniques are supported
to collect some data in the secondary database, while text data is represented
by standalone data.
social data as a systematic data mining architecture, results showed that Facebook
as the social network is the most important source of data. In addition to this
approach, the author has added information about “my wall”, articles
about me, my age and Facebook comments. It was taken as a raw data, which is
then applied to study and monitor the tactics of analysis. The study also
looked at images for advertising their products and for the decision-making
process. A number of data mining techniques predict the constraint of
intellectual knowledge from social data. It essentially organizes essential
facts and other applied activities allowing users to be in touch with their
colleagues on social networks (Facebook). For recovery from the Facebook user
database, use the Facebook API Secret Application key and the Facebook API
Facebook API key. As a result, WEKA files and mining techniques are supported
to collect some data in the secondary database, while text data is represented
by standalone data.
The earlier researchers examined the usefulness of
the user’s personality representation based on features extracted from
Facebook’s data. Classification techniques and their uses have been thoroughly
analyzed in the light of inspiring research results. The study involved a
selection of 250 cases by Facebook users. This test came from about 10,000
status updates provided by the My Personality project. The study has the
following two coherent objectives:
the user’s personality representation based on features extracted from
Facebook’s data. Classification techniques and their uses have been thoroughly
analyzed in the light of inspiring research results. The study involved a
selection of 250 cases by Facebook users. This test came from about 10,000
status updates provided by the My Personality project. The study has the
following two coherent objectives:
(1) Knowing relevant personality-related indicators
that indicate users’ data implicitly or explicitly in Facebook,
that indicate users’ data implicitly or explicitly in Facebook,
(2) Identifying
the feasibility of a prognostic demonstration of supporting future smart
systems.
the feasibility of a prognostic demonstration of supporting future smart
systems.
The study
focused on promoting relevant features in a model to observe the improved production
of classifiers being evaluated. The researchers of explored the applicability
of representing user’s personality based on the extracted features from the
Facebook data. The classification techniques and their utilities were
completely analyzed with regard to the inspirational research outcomes. A
sample of 250 user instances from Facebook formed the research study and this
sample was from about 10,000 status updates, which was delivered by the My
Personality project.
focused on promoting relevant features in a model to observe the improved production
of classifiers being evaluated. The researchers of explored the applicability
of representing user’s personality based on the extracted features from the
Facebook data. The classification techniques and their utilities were
completely analyzed with regard to the inspirational research outcomes. A
sample of 250 user instances from Facebook formed the research study and this
sample was from about 10,000 status updates, which was delivered by the My
Personality project.
The study has the following two interconnected objectives:
(1) Having knowledge about the pertinent
personality-correlated indicators that presents user data implicitly or
explicitly in Facebook,
personality-correlated indicators that presents user data implicitly or
explicitly in Facebook,
(2) Identifying the feasibility of prognostic
character demonstration so that upcoming intelligent systems could be
supported. The study emphasized on the promotion of pertinent features in a
model, through which the enhanced output of the classifiers under evaluation
could be observed.
character demonstration so that upcoming intelligent systems could be
supported. The study emphasized on the promotion of pertinent features in a
model, through which the enhanced output of the classifiers under evaluation
could be observed.
Text
searching and mining on Twitter
searching and mining on Twitter
A major study has been conducted on the Twitter
analysis in recent years. A large number of domains use this data, some of
which use it for academic research and some for applications. New
enhancements to Twitter data are presented in this section. Collecting
documents from different resources triggers the “Text Mining”
process. A certain document would be retrieved by the text mining tools and
this document is pre-processed by checking the character sets and the format
[56]. Then, a text analysis phase would monitor the document. Semantic analysis
makes it possible to derive high quality information from a text. this is
called “text analysis”. The market has many text analysis techniques.
Professionals can use combinations of techniques that are objectives of the
organization. Researchers tend to repeat text analysis techniques until
information is acquired. An information management system can incorporate the
resulting information and, as a result, generate meaningful knowledge for the
user of that information system.
analysis in recent years. A large number of domains use this data, some of
which use it for academic research and some for applications. New
enhancements to Twitter data are presented in this section. Collecting
documents from different resources triggers the “Text Mining”
process. A certain document would be retrieved by the text mining tools and
this document is pre-processed by checking the character sets and the format
[56]. Then, a text analysis phase would monitor the document. Semantic analysis
makes it possible to derive high quality information from a text. this is
called “text analysis”. The market has many text analysis techniques.
Professionals can use combinations of techniques that are objectives of the
organization. Researchers tend to repeat text analysis techniques until
information is acquired. An information management system can incorporate the
resulting information and, as a result, generate meaningful knowledge for the
user of that information system.
The integration of natural language is an
important issue in text mining. The problem of ambiguity is very close in
natural language. There are several meanings of the same word and several words
can have the same meaning. Unclear is called understanding a word that has more
than one meaning possible. Noise appeared in the information extracted because
of this ambiguity. Because ease of use and flexibility are the most important
parts of ambiguity, it is impossible to eliminate it from natural language. A
sentence or meaning may have several understandings, so it is possible that we
get several meanings. The work is still underdeveloped and a particular area is
correlated with the proposed approach as experts have attempted to resolve the
ambiguity problem by conducting a number of research studies. Since there is
uncertainty / ambiguity in the semantic meaning of many discovered words, it is
very difficult to meet the user’s requirements.
important issue in text mining. The problem of ambiguity is very close in
natural language. There are several meanings of the same word and several words
can have the same meaning. Unclear is called understanding a word that has more
than one meaning possible. Noise appeared in the information extracted because
of this ambiguity. Because ease of use and flexibility are the most important
parts of ambiguity, it is impossible to eliminate it from natural language. A
sentence or meaning may have several understandings, so it is possible that we
get several meanings. The work is still underdeveloped and a particular area is
correlated with the proposed approach as experts have attempted to resolve the
ambiguity problem by conducting a number of research studies. Since there is
uncertainty / ambiguity in the semantic meaning of many discovered words, it is
very difficult to meet the user’s requirements.
Scientists developed
and formulated an automated classification technique to identify potentially
abusive user input and assess the likelihood of using social media as a source
of automatic drug abuse surveillance. In this regard, tweets on Twitter were
collected and linked to three commonly used drugs (oxycodone, Adderall and
quetiapine). In addition to interpreting a control medication (metformin),
which is not subject to abuse because of its process, nearly 6,400 tweets were
manually recorded, where these three drugs were reported. Annotated data is
analyzed qualitatively and quantitatively to determine if drug abuse signals
are presented in Twitter publications. In summary, the value of recognition has
been evaluated to study patterns of abuse over time, and an automated
supervised classification technique has also been developed to observe and
separate insertions containing drug abuse of those who do not have it.
According to the survey results, Twitter posts clearly indicated drug abuse.
Compared to the proportion of the control medication (ie metformin: 0.3%),
there are a very large number of tweets containing mission signals for the
three drugs involved (Adderall: 23%, oxycodone: 12% , quetiapine: 5.0%). In
addition, an accuracy of almost 82% (Medical Abuse Class Reminder: 0.51, accuracy:
0.41, F-score: 0.46) was obtained by the automatic classification method.
and formulated an automated classification technique to identify potentially
abusive user input and assess the likelihood of using social media as a source
of automatic drug abuse surveillance. In this regard, tweets on Twitter were
collected and linked to three commonly used drugs (oxycodone, Adderall and
quetiapine). In addition to interpreting a control medication (metformin),
which is not subject to abuse because of its process, nearly 6,400 tweets were
manually recorded, where these three drugs were reported. Annotated data is
analyzed qualitatively and quantitatively to determine if drug abuse signals
are presented in Twitter publications. In summary, the value of recognition has
been evaluated to study patterns of abuse over time, and an automated
supervised classification technique has also been developed to observe and
separate insertions containing drug abuse of those who do not have it.
According to the survey results, Twitter posts clearly indicated drug abuse.
Compared to the proportion of the control medication (ie metformin: 0.3%),
there are a very large number of tweets containing mission signals for the
three drugs involved (Adderall: 23%, oxycodone: 12% , quetiapine: 5.0%). In
addition, an accuracy of almost 82% (Medical Abuse Class Reminder: 0.51, accuracy:
0.41, F-score: 0.46) was obtained by the automatic classification method.
The
study showed how patterns of abuse over time can be analyzed using
classification data. Its purpose is to illustrate the effectiveness of
automatic classification. As a result, drug information can be obtained
significantly on social media, and research has shown that natural language
management and supervised classification are automatic approaches potentially
likely to lead to future monitoring and surveillance mission’s intervention.
Given the supervised learning, the lack of adequate training data is considered
the greatest lack of studies. Annotation and automatic classification are
hampered by the lack of context and ambiguity in the tweets. During the
annotations many ambiguous tweets were found and expert farm competencies were
employed to solve these problems. Because of these ambiguities, the
unclassified procedure is observed in the binary classification process and
this shortcoming will persist until the timed note rules can be specified by
upcoming note rules.
study showed how patterns of abuse over time can be analyzed using
classification data. Its purpose is to illustrate the effectiveness of
automatic classification. As a result, drug information can be obtained
significantly on social media, and research has shown that natural language
management and supervised classification are automatic approaches potentially
likely to lead to future monitoring and surveillance mission’s intervention.
Given the supervised learning, the lack of adequate training data is considered
the greatest lack of studies. Annotation and automatic classification are
hampered by the lack of context and ambiguity in the tweets. During the
annotations many ambiguous tweets were found and expert farm competencies were
employed to solve these problems. Because of these ambiguities, the
unclassified procedure is observed in the binary classification process and
this shortcoming will persist until the timed note rules can be specified by
upcoming note rules.
A study applied text mining approaches to an
extensive set of tweets data. The complete Twitter timeline for 10 university
libraries was used to gather the dataset for this research. Nearly 23,707
tweets formed all the data, with 7625 hashtags, 17,848 forums and 5974
retweets. Inconsistencies between university libraries are found in the
distribution of tweets. “Open” is the word most used by university
libraries from different perspectives. It has been observed that “special
collections” are the most common two-word sequence in aggregated tweets.
While the “save date” was the most recurrent tri-gram (sequence of
three words). In semantic analysis, words such as “insight, knowledge, and
information about cultural and personal relationships” were the most
common categories of words. In addition, “resources” was the most
popular category of tweets among all selected university libraries. The study
highlights the importance of data and text reduction methods used to better
understand the social tasks of academic libraries in order to facilitate
decision-making and strategic planning for service marketing and awareness. The
10 university libraries of the world’s best universities have adopted the text
extraction strategy. The study aimed to illustrate his use of Twitter and to
review their content on Twitter.
extensive set of tweets data. The complete Twitter timeline for 10 university
libraries was used to gather the dataset for this research. Nearly 23,707
tweets formed all the data, with 7625 hashtags, 17,848 forums and 5974
retweets. Inconsistencies between university libraries are found in the
distribution of tweets. “Open” is the word most used by university
libraries from different perspectives. It has been observed that “special
collections” are the most common two-word sequence in aggregated tweets.
While the “save date” was the most recurrent tri-gram (sequence of
three words). In semantic analysis, words such as “insight, knowledge, and
information about cultural and personal relationships” were the most
common categories of words. In addition, “resources” was the most
popular category of tweets among all selected university libraries. The study
highlights the importance of data and text reduction methods used to better
understand the social tasks of academic libraries in order to facilitate
decision-making and strategic planning for service marketing and awareness. The
10 university libraries of the world’s best universities have adopted the text
extraction strategy. The study aimed to illustrate his use of Twitter and to
review their content on Twitter.
As far as social media is concerned, decision-making
is supported and user-generated text is analyzed through text mining and
content analysis. By employing an archiving service (twimemachine.com) in
December 2014, the complete Twitter timelines of 10 academic libraries were
taken into account to collect the dataset for this research. The libraries of
10 highest-ranking universities from the global Shanghai Ranking were chosen
for that purpose. The language of the university must be English-based, which
was the condition for selection and selection was restricted to only one
library if there was more than one library in the university. Certain weaknesses
were found in the study, for example, all of the libraries are English-language
libraries in the sample and only 10 academic libraries were considered for the
analysis. This gap must be filled in future by applying the analysis to a
dataset from diversified academic libraries, including non-English language
libraries. Consequently, a complete understanding of tweet patterns would be
acknowledged.
is supported and user-generated text is analyzed through text mining and
content analysis. By employing an archiving service (twimemachine.com) in
December 2014, the complete Twitter timelines of 10 academic libraries were
taken into account to collect the dataset for this research. The libraries of
10 highest-ranking universities from the global Shanghai Ranking were chosen
for that purpose. The language of the university must be English-based, which
was the condition for selection and selection was restricted to only one
library if there was more than one library in the university. Certain weaknesses
were found in the study, for example, all of the libraries are English-language
libraries in the sample and only 10 academic libraries were considered for the
analysis. This gap must be filled in future by applying the analysis to a
dataset from diversified academic libraries, including non-English language
libraries. Consequently, a complete understanding of tweet patterns would be
acknowledged.
The future inquiry can also incorporate international or
cross-cultural comparisons. Any discrepancy among libraries in their tweets’
content affected by the number and interaction of followers could be
highlighted by the analysis and its findings. The accuracy of the tweet
categorization tool has yielded the inadequate findings, and the said tool
needs to be substantiated through other machine-learning models along with
their applications. Researchers demonstrated in a smoking cessation nicotine
patch study an innovative Twitter recruitment system that is deployed by the
group. The study aimed to describe the methodology and used to address the
issue of digital recruitment. Furthermore, designing a rule-based system with
the provision of system specification besides representing the data mining
approaches and algorithms (classification and association analysis) using
Twitter data. In the case of social media, decision-making is supported and
user-generated text is analyzed through textual content and content analysis.
Using an archiving service (twimemachine.com) in December 2014, the complete
Twitter chronologies of 10 university libraries were taken into account in
order to gather the dataset for this research.
The libraries of the top 10
universities in the Shanghai World Ranking have been selected for this purpose.
The language of the university must be English, which was the condition of
selection and the selection was limited to a single library if there was more
than one library at the university. Some weaknesses were noted in the study,
for example, all libraries are English-language libraries in the sample and
only 10 university libraries were considered for analysis. This gap needs to be
addressed in the future by applying the analysis to a set of data from diverse
academic libraries, including non-English language libraries. Therefore, a complete
understanding of the tweet templates would be recognized. The future survey may
also include international or intercultural comparisons. The analysis and its
results illustrate all the differences between library-influenced tweets
content and subscriber interactions. The accuracy of the tweeting
categorization tool has yielded insufficient results and this tool needs to be
documented through other machine learning models as well as their applications.
Researchers demonstrated, in a nicotine patch intended for smoking, to stop an
innovative Twitter recruitment system used by the group. The study aimed to
describe the methodology and address the problem of digital recruitment. In
addition, a system-based rule-based system is designed to represent data mining
approaches and algorithms (classification and association analysis) using
Twitter data.
cross-cultural comparisons. Any discrepancy among libraries in their tweets’
content affected by the number and interaction of followers could be
highlighted by the analysis and its findings. The accuracy of the tweet
categorization tool has yielded the inadequate findings, and the said tool
needs to be substantiated through other machine-learning models along with
their applications. Researchers demonstrated in a smoking cessation nicotine
patch study an innovative Twitter recruitment system that is deployed by the
group. The study aimed to describe the methodology and used to address the
issue of digital recruitment. Furthermore, designing a rule-based system with
the provision of system specification besides representing the data mining
approaches and algorithms (classification and association analysis) using
Twitter data. In the case of social media, decision-making is supported and
user-generated text is analyzed through textual content and content analysis.
Using an archiving service (twimemachine.com) in December 2014, the complete
Twitter chronologies of 10 university libraries were taken into account in
order to gather the dataset for this research.
The libraries of the top 10
universities in the Shanghai World Ranking have been selected for this purpose.
The language of the university must be English, which was the condition of
selection and the selection was limited to a single library if there was more
than one library at the university. Some weaknesses were noted in the study,
for example, all libraries are English-language libraries in the sample and
only 10 university libraries were considered for analysis. This gap needs to be
addressed in the future by applying the analysis to a set of data from diverse
academic libraries, including non-English language libraries. Therefore, a complete
understanding of the tweet templates would be recognized. The future survey may
also include international or intercultural comparisons. The analysis and its
results illustrate all the differences between library-influenced tweets
content and subscriber interactions. The accuracy of the tweeting
categorization tool has yielded insufficient results and this tool needs to be
documented through other machine learning models as well as their applications.
Researchers demonstrated, in a nicotine patch intended for smoking, to stop an
innovative Twitter recruitment system used by the group. The study aimed to
describe the methodology and address the problem of digital recruitment. In
addition, a system-based rule-based system is designed to represent data mining
approaches and algorithms (classification and association analysis) using
Twitter data.
Twitter Streaming API captured two sets of streaming
tweets, which were collected for the study. Ten search terms (ie, quit, quit,
nicotine, smoking, smoking, stains, cigarette, cigarette, electronic cigarette,
and marijuana) were used to collect the first set. The second set of tweets
contains 30 terms, including the terms of the first set. In addition, the
second set is a superset of the first. A number of studies have been carried
out to investigate methods for collecting information. Since the unstructured
data sets are in text format, many studies have addressed the use of different
text-insertion procedures. Nevertheless, data sets on social networks are not discussed
primarily in these studies. A study of applied different text extraction
techniques would describe the application of these strategies to social
networking sites. In the case of intelligent text analysis, the latest
improvements would also be examined in the study. The study focused on two
important techniques in text mining, namely classification and grouping.
Generally, they are used for studies of unstructured text available in
large-scale settings. Before the World Cup began, about 30,000 tweets were used
by. In addition, an algorithm was used to integrate the consensus matrix and
the DBSCAN algorithm. Therefore, the tweets that affected these predominant
topics were at his disposal. Then the cluster analysis was used to search for
the topics covered by the tweets. Tweets were grouped using k-means,
non-negative matrix factorization (NMF) and a popular classification algorithm.
The results were then compared. Similar results were provided by both
algorithms. However, NMF was faster and researchers could easily interpret the
results.
tweets, which were collected for the study. Ten search terms (ie, quit, quit,
nicotine, smoking, smoking, stains, cigarette, cigarette, electronic cigarette,
and marijuana) were used to collect the first set. The second set of tweets
contains 30 terms, including the terms of the first set. In addition, the
second set is a superset of the first. A number of studies have been carried
out to investigate methods for collecting information. Since the unstructured
data sets are in text format, many studies have addressed the use of different
text-insertion procedures. Nevertheless, data sets on social networks are not discussed
primarily in these studies. A study of applied different text extraction
techniques would describe the application of these strategies to social
networking sites. In the case of intelligent text analysis, the latest
improvements would also be examined in the study. The study focused on two
important techniques in text mining, namely classification and grouping.
Generally, they are used for studies of unstructured text available in
large-scale settings. Before the World Cup began, about 30,000 tweets were used
by. In addition, an algorithm was used to integrate the consensus matrix and
the DBSCAN algorithm. Therefore, the tweets that affected these predominant
topics were at his disposal. Then the cluster analysis was used to search for
the topics covered by the tweets. Tweets were grouped using k-means,
non-negative matrix factorization (NMF) and a popular classification algorithm.
The results were then compared. Similar results were provided by both
algorithms. However, NMF was faster and researchers could easily interpret the
results.
A study of initiated a workflow aimed at better
understanding the large amount of data and qualitative analysis. Twitter posts
from engineering students were the main problem. The fundamental goal was to
identify their problems in their academic experience. The study conducted a
qualitative analysis of samples obtained from approximately 25,000 tweets
associated with engineering students and their academic life. The problems of
the technical students were discovered during the course of the study. For
example, a large volume of study lack of sleep and a lack of social commitment.
In view of these results, a multi-brand classification algorithm was
implemented to classify tweets instead of students’ problems. The algorithm has
been applied to approximately 35,000 tweets continuously on the geo-site of
Purdue University. At first instance, the authorities concerned were informed
of the experiences and questions asked by the students. Social media data was
used to reveal the problems. In addition, a study by also developed a
multi-classifier to organize tweets based on the content evaluation phase.
A
number of known classifiers are consumed significantly in the machine learning
domain and the data recovery process. Compared to other multivariate
classifiers at the cutting edge of technology, Naive Bayes found that the
ratings were known from the dataset. A study by focused on group technology,
performing correlation and association analyzes on social media. The survey on
insurance publications on Twitter was conducted to evaluate this matter. As a
result, the recognition of theories and keywords in social media data has
become a simple task by which the insurers’ information and their application
would be facilitated. Following a detailed analysis, customer requirements and
the potential market would be proactively managed with usability and the
results of the analysis should be effectively implemented in appropriate areas.
According to this evaluation, a total of 68,370 tweets were used. There are two
additional types of evaluations that must be applied to data. The first is
cluster analysis that lets you merge tweets according to their similarities or
differences. An association analysis is the second analysis, while the presence
of some compound words has been discovered. The authors of stated that the
analysis of emotions through the use of social media has attracted great
interest from researchers in recent years. In this context, the authors
discussed the influence of the feeling of tweets on the selection and effects
of the election results on the Web feeling.
understanding the large amount of data and qualitative analysis. Twitter posts
from engineering students were the main problem. The fundamental goal was to
identify their problems in their academic experience. The study conducted a
qualitative analysis of samples obtained from approximately 25,000 tweets
associated with engineering students and their academic life. The problems of
the technical students were discovered during the course of the study. For
example, a large volume of study lack of sleep and a lack of social commitment.
In view of these results, a multi-brand classification algorithm was
implemented to classify tweets instead of students’ problems. The algorithm has
been applied to approximately 35,000 tweets continuously on the geo-site of
Purdue University. At first instance, the authorities concerned were informed
of the experiences and questions asked by the students. Social media data was
used to reveal the problems. In addition, a study by also developed a
multi-classifier to organize tweets based on the content evaluation phase.
A
number of known classifiers are consumed significantly in the machine learning
domain and the data recovery process. Compared to other multivariate
classifiers at the cutting edge of technology, Naive Bayes found that the
ratings were known from the dataset. A study by focused on group technology,
performing correlation and association analyzes on social media. The survey on
insurance publications on Twitter was conducted to evaluate this matter. As a
result, the recognition of theories and keywords in social media data has
become a simple task by which the insurers’ information and their application
would be facilitated. Following a detailed analysis, customer requirements and
the potential market would be proactively managed with usability and the
results of the analysis should be effectively implemented in appropriate areas.
According to this evaluation, a total of 68,370 tweets were used. There are two
additional types of evaluations that must be applied to data. The first is
cluster analysis that lets you merge tweets according to their similarities or
differences. An association analysis is the second analysis, while the presence
of some compound words has been discovered. The authors of stated that the
analysis of emotions through the use of social media has attracted great
interest from researchers in recent years. In this context, the authors
discussed the influence of the feeling of tweets on the selection and effects
of the election results on the Web feeling.
Conclusion
and future work
and future work
The method of communication between them has now
completely changed due to the development of social media. Modernization can
now be seen everywhere and based on it; the production of information touches
the peaks. Currently, new companies are moving forward to actively participate
in the transformation of the mode of communication. The “Keywords and
Expressions” specification can help different companies shape their
future. In this study, we highlighted cutting-edge research on the
implementation of text memory in major social media (Facebook and Twitter).
From several points of view, the text has been explained Exploration mining
according to different models In addition; various authentic references are
provided to support the research work. As a result, text breaks can be
classified into text clusters, text categorization, extraction of associations
and trend analysis according to the applications. The extraction of text will
be well developed with time. Several perspective studies neglect the Arabic
text in social media, allowing many text-mining researchers to fill this gap by
conducting various text mining studies in the context of the Arabic language is
found, with emphasis on the textual memory of English, although the publication
in Arabic on social media is present in bulk.
The scientists explained their
strange and strange characteristics by explaining this attitude. In the
literature studied, we observed that researchers paid less attention to the
analysis of feelings in the Arabic text. Sophisticated tasks of analysis and
disambiguation reinforce the production of target lists of the most recurrent
grammatical structures and meanings of polysemic words, and the potential for
syntactic and semantic ambiguity is high. As future work, we are very interested in the
review of the technique of breaking text on Arabic textual data from Facebook
and Twitter. In addition, future research should take into account the
sensitivity analysis of the Arabic text. The Arabic language is transformed
morphologically, has a free order of words, a punctuation that rarely exists
and short vowels are avoided in written form of standard Arabic. Therefore, the
context is crucial to eliminate the ambiguity that prevails in seemingly
identical forms essential to the recognition of opinions.
completely changed due to the development of social media. Modernization can
now be seen everywhere and based on it; the production of information touches
the peaks. Currently, new companies are moving forward to actively participate
in the transformation of the mode of communication. The “Keywords and
Expressions” specification can help different companies shape their
future. In this study, we highlighted cutting-edge research on the
implementation of text memory in major social media (Facebook and Twitter).
From several points of view, the text has been explained Exploration mining
according to different models In addition; various authentic references are
provided to support the research work. As a result, text breaks can be
classified into text clusters, text categorization, extraction of associations
and trend analysis according to the applications. The extraction of text will
be well developed with time. Several perspective studies neglect the Arabic
text in social media, allowing many text-mining researchers to fill this gap by
conducting various text mining studies in the context of the Arabic language is
found, with emphasis on the textual memory of English, although the publication
in Arabic on social media is present in bulk.
The scientists explained their
strange and strange characteristics by explaining this attitude. In the
literature studied, we observed that researchers paid less attention to the
analysis of feelings in the Arabic text. Sophisticated tasks of analysis and
disambiguation reinforce the production of target lists of the most recurrent
grammatical structures and meanings of polysemic words, and the potential for
syntactic and semantic ambiguity is high. As future work, we are very interested in the
review of the technique of breaking text on Arabic textual data from Facebook
and Twitter. In addition, future research should take into account the
sensitivity analysis of the Arabic text. The Arabic language is transformed
morphologically, has a free order of words, a punctuation that rarely exists
and short vowels are avoided in written form of standard Arabic. Therefore, the
context is crucial to eliminate the ambiguity that prevails in seemingly
identical forms essential to the recognition of opinions.
Topic
How social media has been used for text-mining technology to identify important data themes?
Article writing credit goes to Sadia Nawaz, Imran Zafar, Sameena Ahmad, Zarish Fatima, Taiba Riaz, Ali Raza, Rabia Zahid, Komal Aslam and Noseen Rana