The 21st century has been characterized by an increased attention to social networks. Nowadays, going 24
hours without getting in touch with them in some way has become difficult. Facebook and Twitter, these
social platforms are now part of everyday life. Thus, these social networks have become important sources
to be aware of frequently discussed topics or public opinions on a current issue. A lot of people write
messages about current events, give their opinion on any topic and discuss social issues more and more.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
POLITICAL OPINION ANALYSIS IN SOCIAL NETWORKS: CASE OF TWITTER AND FACEBOOKIJwest
The 21st century has been characterized by an increased attention to social networks. Nowadays, going 24 hours without getting in touch with them in some way has become difficult. Facebook and Twitter, these social platforms are now part of everyday life. Thus, these social networks have become important sources to be aware of frequently discussed topics or public opinions on a current issue. A lot of people write messages about current events, give their opinion on any topic and discuss social issues more and more.
Current trends of opinion mining and sentiment analysis in social networkseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisIJERA Editor
Fast and Appropriate Social Network Analysis (SNA) tools ,techniques, are required to collect and classify
opinion scores on social networksites , as a grouping on wrong opinion may create problems for a society or
country . Social Network Analysis (SNA) is popular means for researcher as the number of users and groups
increasing day by day on that social sites , and a large group may influence other.In this paper, we
recommendhybrid model of opinion recommendation systems, for single user and for collective community
respectively, formed on social liking and influence network theory. By collecting thedata of user social networks
and preferenceslike, we designed aimproved hybrid prototype to imitate the social influence by like and sharing
the information among groups.The significance of this paper to analyze the suitability of ANN and Fuzzy sets
method in a hybrid manner for social web sites classifications, First, we intend to use Artificial Neural
Network(ANN)techniques in social media data classification by using some contemporary methods different
than the conventional methods of statistics and data analysis, in next we want to propagate the fuzzy approach
as a way to overcome the uncertainity that is always present in social media analysis . We give a brief overview
of the main ideas and recent results of social networks analysis , and we point to relationships between the two
social network analysis and classification approaches .This researchsuggests a hybrid classification model build
on fuzzy and artificial neural network (HFANN). Information Gain and three popular social sites are used to
collect information depicting features that are then used to train and test the proposed methods . This neoteric
approach combines the advantages of ANN and Fuzzy sets in classification accuracy with utilizing social data
and knowledge base available in the hate lexicons.
PREDICTING ELECTION OUTCOME FROM SOCIAL MEDIA DATAkevig
In this era of technology, enormous Online Social Networking Sites (OSNs) have arisen as a medium of
expressing any opinions, thoughts towards anything even support their status against any social or
political matter at the same time. Nowadays, people connected to those networks are more likely to prefer
to employ themselves utilizing these online platforms to exhibit their standings upon any political
organizations participating in the election throughout the whole election period. The aim of this paper is to
predict the outcome of the election by engaging the tweets posted on Twitter pertaining to the Australian
federal election-2019 held on May 18, 2019. We aggregated two efficacious techniques in order to extract
the information from the tweet data to count a virtual vote for each corresponding political group. The
original results of the election closely match the findings of our investigation, published by the Australian
Electoral Commission.
Mining and Analyzing Academic Social NetworksEditor IJCATR
Academics establish relationships by way of various interactions like jointly authoring a research paper or report, jointly
supervising a thesis, working jointly on a project, etc. Some of these relationships are ubiquitous whereas other are hard to keep track
of. Of all types of possible academic and research collaborations, co-authorship is best documented. In this paper we analyze the coauthorship
based academic social networks of computer science engineering departments of Indian Institutes of Technology (IITs) as
evidenced from their research publications produced during 2011 and 2015. We use social network analysis metrics to study the
collaboration networks in four leading IITs. From experimental results it can be concluded that IIT Delhi and IIT Kharagpur have a
close knit collaboration network whereas the collaboration network of IIT Kanpur and IIT Madras is fragmented. However, the
collaboration networks of all the four IITs exhibit similar network properties as expected from any other collaboration network
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
POLITICAL OPINION ANALYSIS IN SOCIAL NETWORKS: CASE OF TWITTER AND FACEBOOKIJwest
The 21st century has been characterized by an increased attention to social networks. Nowadays, going 24 hours without getting in touch with them in some way has become difficult. Facebook and Twitter, these social platforms are now part of everyday life. Thus, these social networks have become important sources to be aware of frequently discussed topics or public opinions on a current issue. A lot of people write messages about current events, give their opinion on any topic and discuss social issues more and more.
Current trends of opinion mining and sentiment analysis in social networkseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisIJERA Editor
Fast and Appropriate Social Network Analysis (SNA) tools ,techniques, are required to collect and classify
opinion scores on social networksites , as a grouping on wrong opinion may create problems for a society or
country . Social Network Analysis (SNA) is popular means for researcher as the number of users and groups
increasing day by day on that social sites , and a large group may influence other.In this paper, we
recommendhybrid model of opinion recommendation systems, for single user and for collective community
respectively, formed on social liking and influence network theory. By collecting thedata of user social networks
and preferenceslike, we designed aimproved hybrid prototype to imitate the social influence by like and sharing
the information among groups.The significance of this paper to analyze the suitability of ANN and Fuzzy sets
method in a hybrid manner for social web sites classifications, First, we intend to use Artificial Neural
Network(ANN)techniques in social media data classification by using some contemporary methods different
than the conventional methods of statistics and data analysis, in next we want to propagate the fuzzy approach
as a way to overcome the uncertainity that is always present in social media analysis . We give a brief overview
of the main ideas and recent results of social networks analysis , and we point to relationships between the two
social network analysis and classification approaches .This researchsuggests a hybrid classification model build
on fuzzy and artificial neural network (HFANN). Information Gain and three popular social sites are used to
collect information depicting features that are then used to train and test the proposed methods . This neoteric
approach combines the advantages of ANN and Fuzzy sets in classification accuracy with utilizing social data
and knowledge base available in the hate lexicons.
PREDICTING ELECTION OUTCOME FROM SOCIAL MEDIA DATAkevig
In this era of technology, enormous Online Social Networking Sites (OSNs) have arisen as a medium of
expressing any opinions, thoughts towards anything even support their status against any social or
political matter at the same time. Nowadays, people connected to those networks are more likely to prefer
to employ themselves utilizing these online platforms to exhibit their standings upon any political
organizations participating in the election throughout the whole election period. The aim of this paper is to
predict the outcome of the election by engaging the tweets posted on Twitter pertaining to the Australian
federal election-2019 held on May 18, 2019. We aggregated two efficacious techniques in order to extract
the information from the tweet data to count a virtual vote for each corresponding political group. The
original results of the election closely match the findings of our investigation, published by the Australian
Electoral Commission.
Mining and Analyzing Academic Social NetworksEditor IJCATR
Academics establish relationships by way of various interactions like jointly authoring a research paper or report, jointly
supervising a thesis, working jointly on a project, etc. Some of these relationships are ubiquitous whereas other are hard to keep track
of. Of all types of possible academic and research collaborations, co-authorship is best documented. In this paper we analyze the coauthorship
based academic social networks of computer science engineering departments of Indian Institutes of Technology (IITs) as
evidenced from their research publications produced during 2011 and 2015. We use social network analysis metrics to study the
collaboration networks in four leading IITs. From experimental results it can be concluded that IIT Delhi and IIT Kharagpur have a
close knit collaboration network whereas the collaboration network of IIT Kanpur and IIT Madras is fragmented. However, the
collaboration networks of all the four IITs exhibit similar network properties as expected from any other collaboration network
PREDICTING ELECTION OUTCOME FROM SOCIAL MEDIA DATAkevig
In this era of technology, enormous Online Social Networking Sites (OSNs) have arisen as a medium of expressing any opinions, thoughts towards anything even support their status against any social or political matter at the same time. Nowadays, people connected to those networks are more likely to prefer to employ themselves utilizing these online platforms to exhibit their standings upon any political organizations
participating in the election throughout the whole election period. The aim of this paper is to predict the outcome of the election by engaging the tweets posted on Twitter pertaining to the Australian federal election-2019 held on May 18, 2019. We aggregated two efficacious techniques in order to extract the
information from the tweet data to count a virtual vote for each corresponding political group. The original results of the election closely match the findings of our investigation, published by the Australian Electoral Commission.
PREDICTING ELECTION OUTCOME FROM SOCIAL MEDIA DATAijnlc
In this era of technology, enormous Online Social Networking Sites (OSNs) have arisen as a medium of expressing any opinions, thoughts towards anything even support their status against any social or political matter at the same time. Nowadays, people connected to those networks are more likely to prefer to employ themselves utilizing these online platforms to exhibit their standings upon any political organizations participating in the election throughout the whole election period. The aim of this paper is to predict the outcome of the election by engaging the tweets posted on Twitter pertaining to the Australian federal election-2019 held on May 18, 2019. We aggregated two efficacious techniques in order to extract the information from the tweet data to count a virtual vote for each corresponding political group. The original results of the election closely match the findings of our investigation, published by the Australian Electoral Commission.
This is a presentation given at the ICWSM 2010 in Washington, DC (www.icwsm.org). You can watch a video of the presentation on videolectures.net
Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular social media platform. Social media analytics helps make informed decisions based on people's needs and opinions. This information, when properly perceived provides valuable insights into different domains, such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet) algorithms. The experiments use different data processing steps including trigrams, without trigrams, hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags helps improve the topic inference results with a better coherence score.
Political prediction analysis using text mining and deep learningVishwambhar Deshpande
We have proposed a system to determine current sentiment on twitter using Twit-
ter API for open access which includes opinions from dierent content structures like
latest news, audits, articles and social media posts. and Deep Learning method to
study Historic Data for predicting future results. we utilized Naive Bayes and dictio-
nary based algorithms to predict the sentiment on Live Twitter Data.
Twitter Based Outcome Predictions of 2019 Indian General Elections Using Deci...Ferdin Joe John Joseph PhD
Presented at the 4th International Conference on Information Technology InCIT 2019 organised by Thai-Nichi Institute of Technology and Council of IT Deans in Thailand (CITT)
Online social networks (OSNs) contain data about users, their relations, interests and daily activities and
the great value of this data results in ever growing popularity of OSNs. There are two types of OSNs data,
semantic and topological. Both can be used to support decision making processes in many applications
such as in information diffusion, viral marketing and epidemiology. Online Social network analysis (OSNA)
research is used to maximize the benefits gained from OSNs’ data. This paper provides a comprehensive
study of OSNs and OSNA to provide analysts with the knowledge needed to analyse OSNs. OSNs’
internetworking was found to increase the wealth of the analysed data by depending on more than one OSN
as the source of the analysed data.
Paper proposes a generic model of OSNs’ internetworking system that an analyst can rely on. Two
different data sources in OSNs were identified in our efforts to provide a thorough study of OSNs, which
are the OSN User data and the OSN platform data. Additionally, we propose a classification of the OSN
User data according to its analysis models for different data types to shed some light into the current used
OSNA methodologies. We also highlight the different metrics and parameters that analysts can use to
evaluate semantic or topologic OSN user data. Further, we present a classification of the other data types
and OSN platform data that can be used to compare the capabilities of different OSNs whether separate or
in a OSNs’ internetworking system. To increase analysts’ awareness about the available tools they can use,
we overview some of the currently publically available OSNs’ datasets and simulation tools and identify
whether they are capable of being used in semantic, topological OSNA, or both. The overview identifies
that only few datasets includes both data types (semantic and topological) and there are few analysis tools
that can perform analysis on both data types. Finally paper present a scenario that shows that an
integration of semantic and topologic data (hybrid data) in the OSNA is beneficial.
There are numerous ways to analyse the web information, generally web substance are housed in
large information sets and basic inquiries are utilized to parse such information sets. As the requests
expanded with time, mining web information amended to meet challenging task in a web analysis.
Machine learning methodologies are the most up to date one to go into these analysis forms. Different
approaches like decision trees, association rules, Meta heuristic and basic learning methods are embraced
for making web data appraisal and mining data from various web instances. This study will highlight these
approaches in perspective of web investigation. One of the prime goals of this exploration is to investigate
more data mining approaches alongside machine learning systems, and to express emerging collaboration
of web analytics with artificial intelligence.
Hybrid sentiment and network analysis of social opinion polarization icoictAndry Alamsyah
The rapid growth of social media and user generated contents (UGC) has provided a rich source of potentially relevant data. The problems arise on how to summarize those data to understand and transforming it into information. Twitter as one of the most popular social networking and micro-blogging service can be analyzed in terms of content produced with sentiment analysis. On the other hand, some types of networks can also be constructed to analyze the social network structure and network properties. This research intended to combine those content and structural approaches into hybrid approach for identifies social opinion polarization, this is in the form of conversation network. Sentiment analysis used to determine public sentiment, and social network analysis used to analyze the structure of the network, detecting communities and influential actors in the network. Using this hybrid approach, we have comprehensive understanding about social opinion polarization. As case study, we present real social opinion polarization about reclamation issue in Indonesia.
The Pessimistic Investor Sentiments Indicator in Social NetworksTELKOMNIKA JOURNAL
With the worldwide proliferation of social networks, the social networks have played an important role in the social activities .Peoples are inclined to obtain the corresponding public opinion to make decision such as shopping, education, investment and so on. Analysis of data generated by social networks has become an important field of research, however in the field of public opinion analysis of social networks the quantitative measure indexes are still lacking. In this paper, the calculation method of pessimistic investor sentiments indicator is proposed, and the index has a certain theoretical and practical value.
Need Response 1The subcomponent of crowdsourcing ICT platform.docxvannagoforth
Need Response 1:
The subcomponent of crowdsourcing ICT platform technological architecture I would like to discuss is that gives additionally created an examination concerning public text information (for instance blog postings, comments, appraisals, etc.) and setting up this information sources using sentiment mining tools. The Web has changed the way wherein people express their emotions, offering them the capacity to post comments and reviews on business things and express their points of view on a a huge amount of issues in parties, talk get-togethers, visit rooms, long-broaden agreeable correspondence get-togethers and web diaries. This customer passed on the substance has been seen as a tremendous wellspring of business and political information. Notwithstanding, the tremendous the degree of this information and its normal language structure makes it difficult to remove the consistent areas, for instance, the general inclination/assessment (for instance positive, negative or sensible) on the particular subject (for instance a thing/affiliation or another methodology proposition) and the specific issues raised about it by the customers/visitors of these objectives. It is hidden motivation has been to enable firms to research online overviews and comments entered by customers of their things in various review districts, web diaries, social affairs, etc., in order to arrive at general judgments as for whether customers adored the thing or not (supposition assessment), and moreover continuously express finishes concerning features (traits) of the thing that has been commented on insistently or conflictingly (features extraction and examination).
This subcomponent performs three tasks, firstly it classifies the opinion text, a document which includes various declarations like a dialogue or a blog spot conveying a positive, negative or unprejudiced end. This is suggested as the record level evaluation examination. Secondly further focusing on sentence-level which deals with the gathering of a sentence as objective or passionate, it organizes each sentence in such a structure, that atmosphere it is a unique or targets (demonstrating whether it can express the inclination or not). For each sentence that is a conceptual (infers conveying an inclination) further, the portrayal is done as imparting an appositive, negative or unprejudiced supposition. Lastly extracting the most commented features of the commented articles, and for each commented feature further classification of relevant opinion is executed as positive, negative or unprejudiced.
References
Janssen, M., Wimmer, M. A., & Deljoo, A. (Eds.). (2015). Policy practice and digital science: Integrating complex systems, social simulation and public administration in policy research (Vol. 10). Springer
Need response 2:
nformation and communication technology platform has an important role to play in active crowdsourcing. A policy maker of a government agency initiates ...
PREDICTING ELECTION OUTCOME FROM SOCIAL MEDIA DATAkevig
In this era of technology, enormous Online Social Networking Sites (OSNs) have arisen as a medium of
expressing any opinions, thoughts towards anything even support their status against any social or
political matter at the same time. Nowadays, people connected to those networks are more likely to prefer
to employ themselves utilizing these online platforms to exhibit their standings upon any political
organizations participating in the election throughout the whole election period. The aim of this paper is to
predict the outcome of the election by engaging the tweets posted on Twitter pertaining to the Australian
federal election-2019 held on May 18, 2019. We aggregated two efficacious techniques in order to extract
the information from the tweet data to count a virtual vote for each corresponding political group. The
original results of the election closely match the findings of our investigation, published by the Australian
Electoral Commission.
PREDICTING ELECTION OUTCOME FROM SOCIAL MEDIA DATAkevig
In this era of technology, enormous Online Social Networking Sites (OSNs) have arisen as a medium of expressing any opinions, thoughts towards anything even support their status against any social or political matter at the same time. Nowadays, people connected to those networks are more likely to prefer to employ themselves utilizing these online platforms to exhibit their standings upon any political organizations
participating in the election throughout the whole election period. The aim of this paper is to predict the outcome of the election by engaging the tweets posted on Twitter pertaining to the Australian federal election-2019 held on May 18, 2019. We aggregated two efficacious techniques in order to extract the
information from the tweet data to count a virtual vote for each corresponding political group. The original results of the election closely match the findings of our investigation, published by the Australian Electoral Commission.
PREDICTING ELECTION OUTCOME FROM SOCIAL MEDIA DATAijnlc
In this era of technology, enormous Online Social Networking Sites (OSNs) have arisen as a medium of expressing any opinions, thoughts towards anything even support their status against any social or political matter at the same time. Nowadays, people connected to those networks are more likely to prefer to employ themselves utilizing these online platforms to exhibit their standings upon any political organizations participating in the election throughout the whole election period. The aim of this paper is to predict the outcome of the election by engaging the tweets posted on Twitter pertaining to the Australian federal election-2019 held on May 18, 2019. We aggregated two efficacious techniques in order to extract the information from the tweet data to count a virtual vote for each corresponding political group. The original results of the election closely match the findings of our investigation, published by the Australian Electoral Commission.
This is a presentation given at the ICWSM 2010 in Washington, DC (www.icwsm.org). You can watch a video of the presentation on videolectures.net
Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular social media platform. Social media analytics helps make informed decisions based on people's needs and opinions. This information, when properly perceived provides valuable insights into different domains, such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet) algorithms. The experiments use different data processing steps including trigrams, without trigrams, hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags helps improve the topic inference results with a better coherence score.
Political prediction analysis using text mining and deep learningVishwambhar Deshpande
We have proposed a system to determine current sentiment on twitter using Twit-
ter API for open access which includes opinions from dierent content structures like
latest news, audits, articles and social media posts. and Deep Learning method to
study Historic Data for predicting future results. we utilized Naive Bayes and dictio-
nary based algorithms to predict the sentiment on Live Twitter Data.
Twitter Based Outcome Predictions of 2019 Indian General Elections Using Deci...Ferdin Joe John Joseph PhD
Presented at the 4th International Conference on Information Technology InCIT 2019 organised by Thai-Nichi Institute of Technology and Council of IT Deans in Thailand (CITT)
Online social networks (OSNs) contain data about users, their relations, interests and daily activities and
the great value of this data results in ever growing popularity of OSNs. There are two types of OSNs data,
semantic and topological. Both can be used to support decision making processes in many applications
such as in information diffusion, viral marketing and epidemiology. Online Social network analysis (OSNA)
research is used to maximize the benefits gained from OSNs’ data. This paper provides a comprehensive
study of OSNs and OSNA to provide analysts with the knowledge needed to analyse OSNs. OSNs’
internetworking was found to increase the wealth of the analysed data by depending on more than one OSN
as the source of the analysed data.
Paper proposes a generic model of OSNs’ internetworking system that an analyst can rely on. Two
different data sources in OSNs were identified in our efforts to provide a thorough study of OSNs, which
are the OSN User data and the OSN platform data. Additionally, we propose a classification of the OSN
User data according to its analysis models for different data types to shed some light into the current used
OSNA methodologies. We also highlight the different metrics and parameters that analysts can use to
evaluate semantic or topologic OSN user data. Further, we present a classification of the other data types
and OSN platform data that can be used to compare the capabilities of different OSNs whether separate or
in a OSNs’ internetworking system. To increase analysts’ awareness about the available tools they can use,
we overview some of the currently publically available OSNs’ datasets and simulation tools and identify
whether they are capable of being used in semantic, topological OSNA, or both. The overview identifies
that only few datasets includes both data types (semantic and topological) and there are few analysis tools
that can perform analysis on both data types. Finally paper present a scenario that shows that an
integration of semantic and topologic data (hybrid data) in the OSNA is beneficial.
There are numerous ways to analyse the web information, generally web substance are housed in
large information sets and basic inquiries are utilized to parse such information sets. As the requests
expanded with time, mining web information amended to meet challenging task in a web analysis.
Machine learning methodologies are the most up to date one to go into these analysis forms. Different
approaches like decision trees, association rules, Meta heuristic and basic learning methods are embraced
for making web data appraisal and mining data from various web instances. This study will highlight these
approaches in perspective of web investigation. One of the prime goals of this exploration is to investigate
more data mining approaches alongside machine learning systems, and to express emerging collaboration
of web analytics with artificial intelligence.
Hybrid sentiment and network analysis of social opinion polarization icoictAndry Alamsyah
The rapid growth of social media and user generated contents (UGC) has provided a rich source of potentially relevant data. The problems arise on how to summarize those data to understand and transforming it into information. Twitter as one of the most popular social networking and micro-blogging service can be analyzed in terms of content produced with sentiment analysis. On the other hand, some types of networks can also be constructed to analyze the social network structure and network properties. This research intended to combine those content and structural approaches into hybrid approach for identifies social opinion polarization, this is in the form of conversation network. Sentiment analysis used to determine public sentiment, and social network analysis used to analyze the structure of the network, detecting communities and influential actors in the network. Using this hybrid approach, we have comprehensive understanding about social opinion polarization. As case study, we present real social opinion polarization about reclamation issue in Indonesia.
The Pessimistic Investor Sentiments Indicator in Social NetworksTELKOMNIKA JOURNAL
With the worldwide proliferation of social networks, the social networks have played an important role in the social activities .Peoples are inclined to obtain the corresponding public opinion to make decision such as shopping, education, investment and so on. Analysis of data generated by social networks has become an important field of research, however in the field of public opinion analysis of social networks the quantitative measure indexes are still lacking. In this paper, the calculation method of pessimistic investor sentiments indicator is proposed, and the index has a certain theoretical and practical value.
Need Response 1The subcomponent of crowdsourcing ICT platform.docxvannagoforth
Need Response 1:
The subcomponent of crowdsourcing ICT platform technological architecture I would like to discuss is that gives additionally created an examination concerning public text information (for instance blog postings, comments, appraisals, etc.) and setting up this information sources using sentiment mining tools. The Web has changed the way wherein people express their emotions, offering them the capacity to post comments and reviews on business things and express their points of view on a a huge amount of issues in parties, talk get-togethers, visit rooms, long-broaden agreeable correspondence get-togethers and web diaries. This customer passed on the substance has been seen as a tremendous wellspring of business and political information. Notwithstanding, the tremendous the degree of this information and its normal language structure makes it difficult to remove the consistent areas, for instance, the general inclination/assessment (for instance positive, negative or sensible) on the particular subject (for instance a thing/affiliation or another methodology proposition) and the specific issues raised about it by the customers/visitors of these objectives. It is hidden motivation has been to enable firms to research online overviews and comments entered by customers of their things in various review districts, web diaries, social affairs, etc., in order to arrive at general judgments as for whether customers adored the thing or not (supposition assessment), and moreover continuously express finishes concerning features (traits) of the thing that has been commented on insistently or conflictingly (features extraction and examination).
This subcomponent performs three tasks, firstly it classifies the opinion text, a document which includes various declarations like a dialogue or a blog spot conveying a positive, negative or unprejudiced end. This is suggested as the record level evaluation examination. Secondly further focusing on sentence-level which deals with the gathering of a sentence as objective or passionate, it organizes each sentence in such a structure, that atmosphere it is a unique or targets (demonstrating whether it can express the inclination or not). For each sentence that is a conceptual (infers conveying an inclination) further, the portrayal is done as imparting an appositive, negative or unprejudiced supposition. Lastly extracting the most commented features of the commented articles, and for each commented feature further classification of relevant opinion is executed as positive, negative or unprejudiced.
References
Janssen, M., Wimmer, M. A., & Deljoo, A. (Eds.). (2015). Policy practice and digital science: Integrating complex systems, social simulation and public administration in policy research (Vol. 10). Springer
Need response 2:
nformation and communication technology platform has an important role to play in active crowdsourcing. A policy maker of a government agency initiates ...
PREDICTING ELECTION OUTCOME FROM SOCIAL MEDIA DATAkevig
In this era of technology, enormous Online Social Networking Sites (OSNs) have arisen as a medium of
expressing any opinions, thoughts towards anything even support their status against any social or
political matter at the same time. Nowadays, people connected to those networks are more likely to prefer
to employ themselves utilizing these online platforms to exhibit their standings upon any political
organizations participating in the election throughout the whole election period. The aim of this paper is to
predict the outcome of the election by engaging the tweets posted on Twitter pertaining to the Australian
federal election-2019 held on May 18, 2019. We aggregated two efficacious techniques in order to extract
the information from the tweet data to count a virtual vote for each corresponding political group. The
original results of the election closely match the findings of our investigation, published by the Australian
Electoral Commission.
Twitter is going to become much less important as a person to person (peer to peer) communication medium and instead become more of a content-delivery medium like TV, where content is broadcast to a large number of followers. It's just going to become a new way to follow celebrities, corporations, and the like.
These are the findings of research by two business school professors. Their paper "Intrinsic versus Image-Related Motivations in Social Media: Why do People Contribute to Twitter?", was published in the journal Marketing Science. The professors are Olivier Toubia of Columbia Business School and Andrew T. Stephen of the University of Pittsburgh.
In Professor Toubia’s words, “Get ready for a TV-like Twitter".
The research examined the motivations behind why everyday people, with no financial incentive, contribute to Twitter.The study examined roughly 2500 non-commercial Twitter users. In a field experiment, the profs randomly selected some of those users and, through the use of other synthetic accounts, increased the selected group's followers. At first, they noticed that as the selected group's followers increased, so did the posting rate. However, when that group reached a level of stature — a moderately large amount of followers — the posting rate declined significantly.
"Users began to realize it was harder to continue to attract more followers with their current strategy, so they slowed down.When posting activity no longer leads to additional followers, people will view Twitter as a non-evolving, static structure, like TV."
Based on the analyses, the profs predict Twitter posts by everyday people will slow down, yet celebrities and commercial users will continue to post for financial gain.
The paper is attached on Slideshare.
Running head: ANNOTATED OUTLINE 1
ANNOTATED OUTLINE 5
Annotated Outline
“The Impact of Social Media on Opinion of People”
Student Name
Course Name
Professor Name
Institution Name
Date:
Annotated Outline
Title:
“The Impact of Social Media on Opinion of People”
Introduction:
Social Media is a new place for people to learn, teach, and get knowledge or information about anything. This paper focuses on the importance of social media and its impact on the opinion of the people. More than half of the world is now using different social media platforms such as Facebook, Twitter, Instagram, etc. people spend a lot of time on the social media sites and read the various type of content there. This raises a question that is it influential in any aspect.
Thesis Statement: The social media platforms impact human behavior and their opinion about different things.
Section Heading: Research Question
Question # 1: Do social media affect the opinion of people regarding different aspects?
Question # 2: Do social media affect the opinion of the people positively?
Question # 3: Do social media affect the opinion of the people negatively?
Question # 4: What are the factors that lead to social media influence?
Literature Review
Section Heading: Social Media and Opinion of People
This section of this paper discusses the detail description and definition of social media and people opinion.
Social media is a platform that allows people to connect with each other and share their opinions and thoughts. Social media is in the form of websites and applications. In social media sites, there is content regarding different topics and problems are posted by different people. This content includes social issues, entertainment, history, trend, fashion, events, personal life, and different problems (Lee, 2016).
Section Heading: Connection between Social Media and Opinion of People
This section of the paper focuses on the connection among the social media sites and applications and the opinion of the people that how they are related to each other and what factors of social media impact people opinions.
There are 3.48 billion people in the world who use social media platform to be connected ("Digital 2019: Global Internet Use Accelerates - We Are Social", 2019). According to a survey, people use to spend approximately 2 hours and 22 minutes a day on social media sites and application, and those people are of all ages. Hence, it is evident that there is some connection between people and the social media sits. Behavior, opinion, and attitudes of people are changes on the basis of their experiences, and social media is a place where people get to know and experience different things, issues, topics on different aspects such as politics, social life, history, entertainm.
Sentiment analysis of comments in social media IJECEIAES
Social media platforms are witnessing a significant growth in both size and purpose. One specific aspect of social media platforms is sentiment analysis, by which insights into the emotions and feelings of a person can be inferred from their posted text. Research related to sentiment analysis is acquiring substantial interest as it is a promising filed that can improve user experience and provide countless personalized services. Twitter is one of the most popular social media platforms, it has users from different regions with a variety of cultures and languages. It can thus provide valuable information for a diverse and large amount of data to be used to improve decision making. In this paper, the sentiment orientation of the textual features and emoji-based components is studied targeting “Tweets” and comments posted in Arabic on Twitter, during the 2018 world cup event. This study also measures the significance of analyzing texts including or excluding emojis. The data is obtained from thousands of extracted tweets, to find the results of sentiment analysis for texts and emojis separately. Results show that emojis support the sentiment orientation of the texts and those texts or emojis cannot separately provide reliable information as they complement each other to give the intended meaning.
The impact of sentiment analysis from user on Facebook to enhanced the servic...IJECEIAES
Facebook's influence on the modern social media platform is undoubtedly enormous. While it has gotten a backlash for its inability to control its influence over important affairs, there are still many questions regarding people's perception of Facebook and their sentiment over Facebook. This paper's role in this ongoing debate is to give a glimpse of people's sentiment and perception of Facebook in recent times. By collecting samples data from Facebook's Top Page, this paper hopes to represent a significant amount of people's aspirations towards this company. By processing the data with a processing tool to construct and model out the data and a sentiment analyzer tool helps determine the sentiment, this paper can deduce a 600-comment worth of processed data. The results from the 600 sampled comments concluded that the sentiments towards Facebook are 41.50% negative comments, 22.83% neutral comments, and 35.67% positive comments.
THE SURVEY OF SENTIMENT AND OPINION MINING FOR BEHAVIOR ANALYSIS OF SOCIAL MEDIAIJCSES Journal
Nowadays, internet has changed the world into a global village. Social Media has reduced the gaps among
the individuals. Previously communication was a time consuming and expensive task between the people.
Social Media has earned fame because it is a cheaper and faster communication provider. Besides, social
media has allowed us to reduce the gaps of physical distance, it also generates and preserves huge amount
of data. The data are very valuable and it presents association degree between people and their opinions.The comprehensive analysis of the methods which are used on user behavior prediction is presented in this paper. This comparison will provide a detailed information, pros and cons in the domain of sentiment and
opinion mining.
A sentiment analysis model of agritech startup on Facebook comments using na...IJECEIAES
Facebook page is a tool able to generate perceptions and acceptance, and support people and investors in making business decisions. Moreover, Facebook page plays a part in engaging people in the form of a community. People share experiences and opinions toward products, services, and trends in particular periods on the Facebook page community. Regarding sentiment analysis on Facebook pages, most education and other general topics in English have only been analyzed in English. However, sentiment analysis regarding agritech startups topics in Thai language has not been done yet. This study analyzes opinions and categorizes positive and negative comments by using naive Bayes classifier to examine the sentiments and attitudes of people and investors. The results could possibly reflect the perception rate of agritech startups in Thailand and could be applied to explain attentiveness and assess people’s engagement opinions. Furthermore, it could be applied in studying consumer behavior, marketing analysis, spread of information, and attitudes. The study's model is generic and could be applied in other contexts to provide insightful suggestions.
How could a product or service is reasonably evaluated by anyone in the shortest time? A million dollar
question but it is having a simple answer: Sentiment analysis. Sentiment analysis is consumers review on
products and services which helps both the producers and consumers (stakeholders) to take effective and
efficient decision within a shortest period of time. Producers can have better knowledge of their products
and services through the sentiment analysis (ex. positive and negative comments or consumers likes and
dislikes) which will help them to know their products status (ex. product limitations or market status).
Consumers can have better knowledge of their interested products and services through the sentiment
analysis (ex. positive and negative comments or consumers likes and dislikes) which will help them to know
their deserving products status (ex. product limitations or market status). For more specification of the
sentiment values, fuzzy logic could be introduced. Therefore, sentiment analysis with the help of fuzzy logic
(deals with reasoning and gives closer views to the exact sentiment values) will help the producers or
consumers or any interested person for taking the effective decision according to their product or service
interest.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
How could a product or service is reasonably evaluated by anyone in the shortest time? A million dollar
question but it is having a simple answer: Sentiment analysis. Sentiment analysis is consumers review on
products and services which helps both the producers and consumers (stakeholders) to take effective and
efficient decision within a shortest period of time. Producers can have better knowledge of their products
and services through the sentiment analysis (ex. positive and negative comments or consumers likes and
dislikes) which will help them to know their products status (ex. product limitations or market status).
Consumers can have better knowledge of their interested products and services through the sentiment
analysis (ex. positive and negative comments or consumers likes and dislikes) which will help them to know
their deserving products status (ex. product limitations or market status). For more specification of the
sentiment values, fuzzy logic could be introduced. Therefore, sentiment analysis with the help of fuzzy logic
(deals with reasoning and gives closer views to the exact sentiment values) will help the producers or
consumers or any interested person for taking the effective decision according to their product or service
interest.
How could a product or service is reasonably evaluated by anyone in the shortest time? A million dollar
question but it is having a simple answer: Sentiment analysis. Sentiment analysis is consumers review on
products and services which helps both the producers and consumers (stakeholders) to take effective and
efficient decision within a shortest period of time. Producers can have better knowledge of their products
and services through the sentiment analysis (ex. positive and negative comments or consumers likes and
dislikes) which will help them to know their products status (ex. product limitations or market status).
Consumers can have better knowledge of their interested products and services through the sentiment
analysis (ex. positive and negative comments or consumers likes and dislikes) which will help them to know
their deserving products status (ex. product limitations or market status). For more specification of the
sentiment values, fuzzy logic could be introduced. Therefore, sentiment analysis with the help of fuzzy logic
(deals with reasoning and gives closer views to the exact sentiment values) will help the producers or
consumers or any interested person for taking the effective decision according to their product or service
interest.
Social Media Influence Analysis using Data Science TechniquesMuhammad Bilal
The major purpose of this literature search report is to demonstrate the usage of different tactics of data science to investigate impact of social media while considering the interaction between influences and their followers.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
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When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
POLITICAL OPINION ANALYSIS IN SOCIAL NETWORKS: CASE OF TWITTER AND FACEBOOK
1. International Journal of Web & Semantic Technology (IJWesT) Vol.12, No.4, October 2021
DOI : 10.5121/ijwest.2021.12401 1
POLITICAL OPINION ANALYSIS IN SOCIAL
NETWORKS: CASE OF TWITTER AND FACEBOOK
Telesphore TIENDREBEOGO and Yassia ZAGRE
University of Nazi Boni, Bobo Dioulasso, Burkina Faso
ABSTRACT
The 21st century has been characterized by an increased attention to social networks. Nowadays, going 24
hours without getting in touch with them in some way has become difficult. Facebook and Twitter, these
social platforms are now part of everyday life. Thus, these social networks have become important sources
to be aware of frequently discussed topics or public opinions on a current issue. A lot of people write
messages about current events, give their opinion on any topic and discuss social issues more and more.
The emergence and enormous popularity of these social networks have led to the emergence of several
types of analysis to take advantage of them. One of them is the analysis of opinions in texts. It aims at
automatically classifying opinions in order to position them on a sentiment scale, thus allowing to
characterize a set of opinions without having to rely on a human to read them. Currently, opinion analysis
offers us a lot of information related to public opinion, either in the commercial world or in the political
world. Many studies have shown that machine learning techniques, such as the support vector machine
(SVM) and the naive Bayes classifier (NB), perform well in this type of classification.
In our study, we first propose an approach for tracking and analyzing political opinions in social networks.
Then, we propose a trained and evaluated machine learning model for political opinion classification. And
finally, the study aims at setting up a web interface to collect and analyze in real time political opinions
from social networks
KEYWORDS
Opinion analysis, opinion mining, Naive Bayes, Support Vector Machine, text classification, social
networks, machine learning, polarity
1. INTRODUCTION
Lately, social networks have become deeply rooted in the lives of their users. According to
Hootsuite[1]in January 2020 there were 3.8 billion active users in the world of social networks.
On the same date, Burkina Faso had 1.6 million active users of social media with a growth of
37% from 2019 to 2020. As a result, users are not only expressing their opinions on any topic, but
also increasingly discussing social issues and political events These exchanges create a large
volume of potentially exploitable information.
The development of social networks, which can be considered as a democratization of
computing, generates a rich mine of exploitable data called “Big Data”. This data is critical to
decision making for many individuals and organizations[2]. For this purpose, new analysis
technologies, alternatives to polls, have indeed appeared in recent years within the media and
political fields. As a result, researchers have since tried to conceptualize methods to analyze this
“Big Data” in a methodical, structured and efficient way[3]. Thus, among many methods, opinion
analysis has been developed to specifically analyze the polarity (i.e.: positive or negative)of texts
2. International Journal of Web & Semantic Technology (IJWesT) Vol.12, No.4, October 2021
2
posted online on various levels. The objective of this type of analysis is generally to classify texts
or parts of texts according to the polarity of their author with respect to a given topic.
Our objective in this paper is to propose on the one hand an approach to monitor and analyze
political data generated in social networks. On the other hand, we propose a machine learning
model to analyze political opinions from social networks.
Our paper is structured as follows: we first present related work on opinion analysis. We then
provide an overview of techniques and applications. Finally, we present and explain the
implementation of a machine learning model for the analysis of our data. We also offer a web
interface to collect and analyze political opinions from social networks in real time.
2. RELATED WORKS
2.1. Social Networks
Social networks such as Twitter and Facebook have been the subject of a lot of research. Several
main factors explain this interest. The first is that these networks are almost entirely composed of
user-generated content. This would mean that every message posted on these sites has an opinion
or subjectivity of some kind. Moreover, the user is free to post whatever he or she wants, which
generates very diverse themes and topics, most of which are related to current events. In[2], the
authors point out that this portrait of opinion at a specific moment in time can be used to analyze
the evolution of opinion on certain subjects, provided that a periodic analysis of the latter is
performed. Secondly, the majority of social networks make their APIs available which facilitates
data collection as mentioned by Fang and Zhan (2015)[4].Finally, the number of active people
online, who generate content, is massive [5]. Such social networks are therefore real data mines
that allow researchers, individuals as well as large companies to collect data among a wide range
of topics.
2.2. Social Networks and Political Opinions
The potentials of social media seem to be the most promising in the political context as they can
be a catalyst for more participation and democracy [6]. In [7], Stefan Stieglitz defines public
participation as the process by which the concerns, needs, and values of the public are
incorporated into the decision-making of governments and corporations. In a similar vein,
Karpf[8] introduces the notion of “Political 2.0” which can be understood as exploiting the
reduced transaction costs of the Internet and its abundance of information, in order to create more
participatory and interactive political institutions. There is a large body of research regarding the
role of social networks in political thinking. The US presidential campaign revealed that social
networks have become increasingly essential to political communication and persuasion. Recent
studies have shown an emerging need for political institutions as well as government departments
to leverage the resources of social networks to improve services and communication with citizens
and voters [8]. Similarly, Williams and Gulati [9] assert that it has become increasingly important
to stay abreast of ongoing discussions and manage one’s own reputation in virtual communities,
especially with respect to emerging topics that may lead to scandal or crisis for a specific
politician or party. In a survey of members of the German “Bundestag”, Stieglitz and Stefan [8]
found that most members of parliament would like to benefit from concepts and instruments to
identify sensitive political topics early on. However, this task requires a lot of effort, appropriate
tools and, above all, a systematic approach.
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2.3. Opinion Analysis
Interest in the subject of sentiment analysis seems to be increasing year after year since 2002. At
least that’s what we could tell by looking at the evolution of the search for the phrase “sentiment
analysis” in the google search engine. See Figure 1.
Figure 1:Evolution of searches made with the expression sentiment analysis in the Google search engine
since 2002in the world
It seems that the attraction for this topic is also shared by the research world. Indeed, more than
7000 articles have been published on this subject until 2019[10]. The analysis of the opinion thus
occupies a place of a certain importance from the interest that is granted to it and by all the
literary activity that is dedicated to it. In the literature, sentiment analysis, opinion mining,
opinion extraction, sentiment mining, subjectivity analysis, emotion analysis, review mining, are
terms used to designate technologies for the automatic analysis of written or spoken discourse in
order to extract subjective information such as judgments, evaluations or emotions.
According to Cambria[11], opinion analysis has been one of the most active research areas in
natural language processing since the early 2000. The purpose of opinion analysis is to design
automatic tools to extract information from a natural language text, such as opinions and feelings,
in order to generate knowledge that can be used by a decision support system or by a decision
maker. Opinion analysis is the computer processing of a text that is explored in order to identify
the general sentiment or opinion expressed. We often speak of classifying the polarity (positive,
negative or neutral) of a text. The exploration is done on an entity and these entity can represent
individuals, products, events or various subjects that are likely to be covered by comments,
criticisms or appreciations.
In order to identify the polarity of a text, opinion analysis relies on several features which are
among others: the frequency of a term, the importance of a term in the text, negations in
sentences [12]. In other words, opinion analysis concerns the processing of an opinion text to
extract and categorize the opinions of a certain document. The polarity of the sentiments usually
expressed in terms of positive or negative opinion (binary classification).However, there can be a
multiple classification, where the feeling can have a neutral label or even a different label like
very positive, positive, neutral, negative, very negative, the labels can also be associated with
emotions such as sadness, anger, happiness, etc.
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2.3.1. Opinion Analysis Levels
The research on opinion analysis was mainly conducted at three different levels of analysis [2]:
Message level or Document level, Sentence level and aspect level.
2.3.2. Opinion Analysis and Applications
According to Pang and Lee [2], it was in 2001 that the field of automatic opinion analysis began
to attract attention. The numerous possibilities brought by this method and the doors it could
open eventually aroused the interest of many political, sociological, governmental, financial,
advertising and commercial fields.
• Politics: Nowadays, political actors follow the trend of opinion analysis, because before
declaring a new law, politicians try to collect the opinion of social network users about this
law. Through opinion analysis, policy makers can collect citizens’ opinions on certain
policies, in order to benefit from this information to improve or create a new policy that
suits with citizens. It is highly strategic to also know the opinion of Internet users on a
politician during a presidential election [13][14]. It should be noted that people’s opinion
and experience is a very useful element in the decision-making process.
• Economy: Thanks to the analysis of opinion, the companies can know the opinion of the
customer son their products or their services. In a perspective of improvement of their
products and increase of their sales and incomes. Marketing has quickly understood the
interest of opinion analysis. An example of commercial interest found in Pang and Lee’s
study [2] is that of a marketer wanting to know why his new product was not selling well.
He was therefore interested in the opinions of his consumers regarding his product. The
result was useful for him because by knowing their opinions, he was able to orient his offer
according to the requirements of the market.
• Other areas of application: Recently, opinion analysis is also being applied in totally
different fields, namely on-profit fields. The two most sought-after domain sat the moment
are the detection of cyber stalking as well as the detection of suicidal tendencies of people
active on the web[15][16][17][18].
2.3.3. Opinion Analysis Approach
In the literature review, there are generally three approaches to opinion analysis:
Lexicon based approach;
Machine Learning based approach;
Hybrid approach
Lexical approach: According to Annett and Kondrak, Sasikala and Mary, the lexical approach
consists of identifying words present in a dictionary of pre-labeled words(each assigned a value
on a scale composed of several sentiment intensities) and tallying the sentiment numbers to arrive
at a total polar score for each text[19][20]. This approach therefore requires the availability of a
dictionary that is adapted to the language used in the texts understudy. Moreover, according to
Muhammad et al, for the dictionary to be effective, it must be adapted to the domain under study
[21].
Machine learning approach: A major shortcoming of this lexicon-based approach is its inability
to identify opinion words with domain- and context-specific orientations. As, a sub-field of
artificial intelligence (AI), machine learning based approach refers to the techniques and actions
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that researchers have to develop an automatic system that, by analyzing data, learns by itself.
These learning algorithms allow systems to train themselves with the data provided in order to
develop and improve. From a set (or model) of data called “dataset”, the algorithms are trained
and tested. the algorithms are trained and can, from these models, make predictions about other
data they may encounter in the future. The machine learning approach is subdivided into
supervised, unsupervised and semi supervised learning methods.
Hybrid Approach: This approach combines the lexicon based approach and the machine learning-
based approach and tries to correct the drawback of the lexicon-based approach of low recall and
the machine learning-based approach of manual annotation.
Classification algorithms: There are different classifiers in machine learning methods that are
used for opinion analysis. The Support Vector Machine method was introduced by Joachims
[22][23], then used by Drucker [24], Taira and Haruno [25], and Yang and Liu[26]. The Naïve
Bayes classifier is a supervised probabilistic learning technique that is regularly used for
classification purposes. It is based on Bayes’ theorem where the conditional probability of an
event X occurring, for an index Y, is determined by Bayes’ rule [2] (See equation 1).
P (X/Y ) = P(X)P(Y/X)/P(Y ) (1)
The k-Nearest Neighbors KNN method is a classification method in which the model stores
observations from the training set for classification of the test set data. A text classifier based on
the decision tree method is a tree of internal nodes that are labeled with terms, the branches
coming out of the nodes are tests on the terms, and the leaves are labeled with categories
[27].Neural networks constitute a network of units, where input units correspond to terms, output
units to categories of interest, and weights of edges connecting units to dependency relationships.
To rank a test document d_i, its weights W_ki are loaded into the input units; activation of these
units propagates through the network, and the value of the output unit(s) determines the ranking
decision [4].
2.3.4. Opinion Analysis Problems
In the literature, opinion analysis is a very complex task with many unsolved problems[2]. The
following are some of the challenges in dealing with this area. The handling of comparisons and
the handling of negation, the detection of false opinions[2], the problem of language, Aboutness,
the detection of sarcasm[28].In this section, we have first presented what opinion analysis is, with
its three different levels of analysis and its applications. Then, we presented the approaches that
exist in the literature with the different algorithms.
And finally, we evoked according to the literature the problems which existed in this type of task.
Studies have shown that social networks such as Facebook and Twitter are a reliable indicator of
the general opinion of users on entities, events or institutions, etc.
3. METHODOLOGY
In this section, we propose an approach to tracking and analyzing political opinions from Twitter
and Facebook. While data tracking and monitoring concerns different approaches to how and
what kind of politically real data generated by users of different social network platforms can be
tracked and collected, the section on data analysis discusses different methods of analysis for
different purposes and approaches.
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3.1. Data Tracking Approach
Tracking sources: The first step in data collection is to determine the sources of the data. For this
reason, we decided to develop our approach by focusing on Twitter and Facebook as the primary
sources of social media detain the case of Twitter, the data to be monitored and exploited is in the
form of public "tweets" that are easily accessible. For Facebook, the most important type of data
is the content of the “wall”, including “status updates” and related comments. It is important to
note that data on Facebook is, unlike Twitter, semi-public in nature,i.e., data can only be
collected from public profiles (or more concretely, groups or “pages”, since most individual
profiles are not public). Specifically, in a political context, a list of politically relevant Facebook
groups and pages should be predefined as data sources. Tracking methods: Both Twitter and
Facebook offer application programming interfaces (APIs) for tracking data. The most frequently
used APIs for Twitter are the Search API and the Streaming API, while Facebook’s Graph API
allows programmers to conveniently track wall posts (i.e. status updates and comments).
Tracking Approaches: We have identified four approaches to tracking political data, which
depend on the specific intentions of the tracker: (1) self-reputation,(2) keyword/topic-based, (3)
actor-based, and (4) random/exploratory.
self-reputation Approach: The first approach is applicable when, for example, politicians
or political parties want to know explicitly how people talk about them in social networks.
In this case, politicians or parties can have all tweets that contain their name collected,
either as a simple keyword or as a hashtag. If they have their own presence on Facebook in
terms of a page or group, they should follow all posts and corresponding comments
published by users or members of their own page or group.
Keyword/Topic-based Approach: Politicians are often very interested in the reactions or
opinions of Internet users on certain political topics. In this case, the second tracking
method seems appropriate, as it tracks Tweets as well as Facebook posts that contain
keywords related to the topics of interest. To achieve a high level of data completeness, the
relevant keywords of the topic of interest must be carefully selected. The more important
the topic to be analyzed, the higher the number of keywords to be taken into account.
Actor-based Approach: In political communication, a number of actors can generally be
considered more influential or popular than most other users. These actors are said to have
the power to influence opinion-forming processes (online)[2]. Therefore, politicians or
political parties are also interested in tracking these important users in terms of generated
content. For this, an actor-based tracking approach could be used to track tweets, wall
posts as well as corresponding comments specifically provided by these influential users
who should also be identified in advance.
Random/Exploratory Approach: Unlike the three tracking approaches that are more
targeted in nature, the fourth approach supports content mining. The principle of this
tracking approach is to randomly determine one or more data sets (tweets or Facebook
posts) for different time periods for analysis. Based on this random data, a content analysis
can be used to identify key political topics and detect the opinions or feelings of users
associated with these topics.
3.2. Opinion Analysis Approach
A major drawback of the lexicon-based approach is its inability to identify opinion words with
domain- and context-specific orientations. Based on the difficulties of the lexicon-based method
reported in the literature review, it is appropriate to use a machine learning technique. For this
purpose, we have chosen machine learning algorithms according to their popularity in the
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literature and according to their predictive potential in the context of opinion analysis. We chose
four algorithms, including support vector machines (SVM), decision tree (DT), random forest
(RF) and Naïve Bayes (NB). Random Forest (RF) and Naïve Bayes (NB). Finally, we compared
the classification results of the four algorithms in order to select the one that gives the best result
in the classification of political opinions.
3.3. Data Representation
The data representation methodology that we adopt takes into account the information that was
synthesized in our literature review. Indeed, we have chosen to prepare the data according to the
types of representations and transformations that we thought were the best given the
characteristics of our data, namely Term Frequency Inverse Document Frequency (TF-IDF).
Considering the terms j in a document i being part of a certain corpus of documents, here is how
we could represent the TF-IDF under an algebraic form[4] (see equation 2).
𝑽𝒊𝒋 = 𝑻𝑭𝒊𝒋 . 𝑰𝑫𝑭𝒋 such as 𝑻𝑭𝒊𝒋 =
𝒇𝒊𝒋
𝒏𝒊
𝑎𝑛𝑑 𝑰𝑫𝑭𝒋 = 𝒍𝒐𝒈(
𝒎
∑ 𝒃𝒊𝒋
𝒎
𝒊=𝟏
) (2)
where:
𝑽𝒊𝒋 = the value of the term j in the document i.
𝒇𝒊𝒋 = number of times the term j in the document i.
𝒏𝒊 = total number of terms in the document i.
𝒃𝒊𝒋 =1 if the term j is present in the document i and 0 else.
𝒎= if the term j is present in the corpus.
3.4. Data Pre-Processing
The characteristics of tweets generally boil down to their limited length and the use of informal
language. Thus, Twitter users use abbreviations, emoticons, and slang to express their opinions
and feelings. Therefore, a pre-processing step is essential. In the following we present the
preprocessing procedure followed in our work, whose goal of this step is to clean up the tweets
and make them as close as possible to a formal language. First we started by eliminating URLs
and words with hashtag, non-alphabetic characters, repeated terms and punctuations.
3.5. System Architecture
Our study focuses on the task of opinion analysis on social network data in particular Twitter and
Facebook. To achieve this goal and to get the best possible performance, we propose the
following architecture. Figure 2 below illustrates the general process of the system which
includes several steps including data collection, pre-processing, annotation, sentiment analysis
and visualization.
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Figure 2: System architecture
The collection phase scans Twitter and Facebook to extract tweets and comments thanks to the
API of the social network. The result of the collection goes through a step of pre-processing
which allows to remove the duplicates and the spams (same author, same content, same post).
Once the data has been pre-processed, comes the annotation phase, which allows to create a
training dataset for the opinion analysis system. The text stream from the comments is then sent
to categorization to determine the sentiment class, which in turn uses the data from the annotation
layer for learning. The visualization phase draws from the latter to expose the results to the user
according to the desired parameters. The final application will take the form of a Dashboard that
present she aggregated statistics of the analysis. In this section, we have proposed an approach to
tracking and analyzing political opinions from social media. Specifically, we have described
various data tracking approaches and corresponding analysis methods that could help to better
understand political discussions in socialmedia. From a practical perspective, this approach
should serve as a guideline for developing tools to collect, store, analyze, and summarize user-
generated social media content that is politically relevant to political institutions. From a research
perspective, the framework provides a comprehensive overview of different specific
methodological approaches that can be used to analyze political opinions from social networks.
4. BACKGROUND
These first years of the 21st century seem to be marked by multiple changes in the production and
exchange of information. Thus, since the advent of Web 2.0 and especially the massive use of
social networks, the number of users has considerably increased and their role has evolved into
that of information provider. As a result, an increasing number of users send hundreds of millions
of messages every day to specially created websites, such as Facebook and Twitter. Users write
about their vision of society, increasingly discuss social issues and political events, share
opinions and divulge ideas [4].
As a result, social networks have become an essential research material for researchers in
computer science and the humanities. To this end, new analysis technologies, alternatives to polls
and surveys which are reputed to be long and costly, have indeed appeared in recent years within
the media and political fields. Thus, among many methods, opinion analysis has been developed
to specifically analyze the polarity of texts. Hence the growing development of techniques to
capture these evaluations of Internet users, ranging from the simple counting of positive or
negative comments to the more detailed analysis of the contents of these comments. It is in this
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context that this paper is written. The objective of this paper is to propose an approach to monitor
and analyze political data generated in social networks. On the other hand, we propose a machine
learning model to analyze political opinions from social networks.
5. RESULTS AND DISCUSSION
In this section, we first present the data that was used to train our model. The input data we used
are tweets, extracted from a dataset to train and test our model. We also test the proposed model
in real time on tweets extracted from Twitter itself. These tweets represent the statuses posted on
this social network and related to the political field.
5.1. Dataset
To train our algorithms, it is important to have well annotated data. To do so, we thought it wiser
to take texts already marked by their authors rather than marking +9them ourselves. For the sake
of rigor, we also considered the size of the sample as an essential factor to improve the value of
the results. Our choice was therefore theSentiment140 dataset which is part of the
Sentiment140[29] project at Stanford University. Indeed, this database contains thousands of
tweets with their respective ratings, and therefore has the necessary assets for the application of
our classification. It is a large dataset, more than one million tweets with two classes of negative
and positive tweets, with 800,000 for each. The authors represented the positive class with the
number 4 and the other class with the number 0.
5.2. Presentation of Results
Recall that the objective of our study is to implement a system to classify tweets or comments on
political topics. Such a system consists of two phases in the implementation. The first step is a
machine learning model trained with algorithms and among them the one that performs best. The
second phase is the implementation of a web application using a machine learning model and
allowing to visualize the trends in real time. We will present here the results of the machine
learning models.
For the validation of the performances of our model, we use the 80% 20% method, as 80% used
in the learning phase, and 20% for the testing phase. In agreement with [30]. Table I presents the
confusion matrix that applies to our binary classification context. The performance measures used
are precision, recall and F1-measure, whose bases of calculation are relative to the confusion
matrix.
Table1 : Confusion matrix in a polar classification contexte.
Positive Negative
Positive True Positive (TP) False Positive (FP)
Negative False Negative (FN) True Negative (TN)
The measurements are presented in the following formulas:
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑇𝑃
𝑇𝑃+𝐹𝑃
(3)
This measure represents the proportion of correct predictions to the total number of predictions.
predictions.
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Recall =
TP
(TP + F N)
(4)
This metric corresponds, for a binary classification, to the number of positive predictions of the
model out of the total of predictions that should be positive. Otherwise, it is the proportion of
well-ranked items to the number of items in the class being predicted.
To combine all this information, we often introduce a metric called the F1-Score. It is calculated
in this way.
𝐅𝟏 − 𝐬𝐜𝐨𝐫𝐞 =
1
1
1
(
1
𝑅𝑒𝑐𝑎𝑙𝑙
+
1
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦
)
= 2 ∗ 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 ∗ 𝑅𝑒𝑐𝑎𝑙𝑙(𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 + 𝑅𝑒𝑐𝑎𝑙𝑙(4)
Represents the trade-off measure between precision and recall. In the table II, we summarize the
result obtained for each model.
Table 2: The validation results of dataset.
Algorithms NB SVM RF DT
Accuracy 83.26% 82.75% 69.78% 77.08%
Recall 0.7 0.8 0.6 0.7
F1-score 0.7 0.8 0.6 0.7
CPU time 1mn 50s 4h 54mn 30s 8mn54s 2h 56mn 55s
Our dataset consisted of nearly 1.6 million tweets evenly distributed in the 2 categories. In order
to select the best algorithm, we used several different algorithms. Thus, after running several tests
with different models and configurations, we selected Naïve Bayes as the best performing
classifier for this particular problem. To evaluate the results, we used the 5-way cross-validation
method and our best performing classifier reaches an accuracy of 83.26%.
6. CONCLUSIONS
The arrival of the digital age, coupled with the rapid expansion of the web and its
democratization, has profoundly changed the way society works. Now, the Internet is full of user-
generated content. This content, called “Big Data”, has become an invaluable mine of data that
can be exploited in many financial, political and no lucrative fields. At the same time, a number
of methods to successfully analyze this data have emerged. Among these methods, opinion
analysis has been developed to specifically analyze the polarity of texts posted online on various
levels. These methods have become even more relevant due to the rise in popularity of social
networks. The leaders in this field, Facebook and Twitter, have billions of users who are
constantly generating new content while expressing their opinions.
In this paper, we focused on the analysis of political opinion in social networks. To achieve our
goal, we proposed an approach for tracking and analyzing political opinion from social networks.
Specifically, we have described various approaches to data tracking and the corresponding
analysis methods On the other hand, we trained a machine learning model for political opinion
analysis from social networks. To do so, we used four machine learning algorithms (SVM, DT,
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RF, NB) that we trained with the sentment140 [36] dataset. At the end of the training, we chose
NB which is the best performing classifier in terms of accuracy. It reaches an accuracy of83.26%.
Ultimately, the results obtained demonstrate that social networks, and Twitter and Facebook in
particular, are a source of data for obtaining key indicators for better decision-making.
However, we could agree with some authors[3][4][12]that the opinions coming from social
networks would not constitute a fairly representative sample of the distribution of the whole
population, especially in our countries where the digital penetration rate remains very low, i.e.
22%penetration rate in Burkina Faso with 1. 6 million active users of social networks in January
2020 [31]. This could also be a significant bias. Nevertheless, we are witnessing a very rapid
evolution of the penetration rate. This is the case in Burkina Faso, which has seen a strong 35%
grow thin active social media users from one year to the next [31].
As perspectives to our work, we foresee:
test our model on other data sets;
develop the model to manage the problem of sarcasm;
Finally, we believe that it is necessary to define a framework that will allow an efficient analysis
of the data from social networks in order to derive several useful information for decision
making.
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AUTHOR
YassiaZagre, master degree in information systems and decision support system in
nazi BONI university Burkina Faso. My interest research topic is image
watermarking for decision support and multimedia system.
Telesphore Tiendrebeogo PhD and overlay network and assistant professor at
Nazi Boni University. I have a master's degree in multimedia and real time system.
My current research is on big data and image watermarking