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.
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.
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)
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.
Using Tweets for Understanding Public Opinion During U.S. Primaries and Predi...Monica Powell
Abstract
Using social media for political analysis, especially during elections, has become popular in the past few years where many researchers and media now use social media to understand the public opinion and current trends. In this paper, we investigate methods for using Twitter to analyze public opinion and to predict U.S. Presidential Primary Election results. We analyzed over 13 million tweets from February 2016 to April 2016 during the primary elections, and we looked at tweets that mentioned either Hillary Clin- ton, Bernie Sanders, Donald Trump or Ted Cruz. First, we use the methods of sentiment analysis, geospatial analysis, net- work analysis, and visualizations tools to examine public opinion on twitter. We then use the twitter data and analysis results to propose a prediction model for predicting primary election results. Our results highlight the feasibility of using social media to look at public opinion and predict election results.
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.
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.
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)
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.
Using Tweets for Understanding Public Opinion During U.S. Primaries and Predi...Monica Powell
Abstract
Using social media for political analysis, especially during elections, has become popular in the past few years where many researchers and media now use social media to understand the public opinion and current trends. In this paper, we investigate methods for using Twitter to analyze public opinion and to predict U.S. Presidential Primary Election results. We analyzed over 13 million tweets from February 2016 to April 2016 during the primary elections, and we looked at tweets that mentioned either Hillary Clin- ton, Bernie Sanders, Donald Trump or Ted Cruz. First, we use the methods of sentiment analysis, geospatial analysis, net- work analysis, and visualizations tools to examine public opinion on twitter. We then use the twitter data and analysis results to propose a prediction model for predicting primary election results. Our results highlight the feasibility of using social media to look at public opinion and predict election results.
POLITICAL OPINION ANALYSIS IN SOCIAL NETWORKS: CASE OF TWITTER AND FACEBOOK dannyijwest
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.
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.
NEGOTIATING THE ANALOG MAINSTREAM WITH DIGITAL METHODS IN HAND VISIONS FROM T...Pertti Ahonen
Dr. Pertti Ahonen, Professor, Faculty of Social
Sciences, University of Helsinki, Finland,
pertti.ahonen@helsinki.fi
ESS Digital Sociology Mini-Conference,
Boston, March 17–20, 2016
Session 43, March 17, Thursday, 3:30 – 5:00 p.m.
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.
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.
• Created a data warehouse of Women Empowerment and Gender Gap, which gives better suggestions and more details for the prospective gender gap.
• Data collected from 6 different data sources ( both structured and unstructured data) using web scraping.
• Cleaned the dataset using R and automated script to load cleaned datasets and processed (ETL).
• Data was analyzed using Visual Studio and Microsoft SQL tools (SSIS, SSMS, SSAS) to create a data warehouse and deploy cube and finally visualized analysis using Tableau.
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.
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.
This paper presents the results of a new monitoring project of the US presidential elections with the aim of establishing computer-based tools to track in real time the popularity or awareness of candidates. The designed and developed innovative methods allow us to extract the frequency of queries sent to numerous search engines by US Internet users. Based on these data, this paper demonstrates that Trump was more frequently searched than the Democratic candidates, either Hillary Clinton in 2016 or Joe Biden in 2020. When analyzing the topics, it is observed that in 2020 the US users had shown a remarkable interest in two subjects, namely, Coronavirus and Jobs (unemployment). Interest for other topics such as Education or Healthcare were less pronounced while issues such as Immigration were given even less attention by users. Finally, some “flame” topics such as Black Lives Matter (2020) and Gun Control (2016) appear to be very popular for a few weeks before returning to a low level of interest. When analyzing tweets sent by candidates during the 2020 campaign, one can observe that Trump was focused mainly on Jobs and on Riots, announcing what would happen if Democrats took power. To these negative ads, Biden answered by putting forward moral values (e.g., love, honesty) and political symbols (e.g., democracy, rights) and by underlying the failure of the current administration in resolving the pandemic situation.
Analyzing Political Sentiment of Indic Languages with TransformersIJCI JOURNAL
This paper presents an analysis of sentiment on Twitter towards the Karnataka elections held in 2023, utilizing transformer-based models specifically designed for sentiment analysis in Indic languages. Through an innovative data collection approach involving a combination of novel methods of data augmentation, online data preceding the election was analyzed. The study focuses on sentiment classification, effectively distinguishing between positive, negative, and neutral posts, while specifically targeting the sentiment regarding the loss of the Bharatiya Janata Party (BJP) or the win of the Indian National Congress (INC). Leveraging high-performing transformer architectures, specifically IndicBERT, coupled with specifically fine-tuned hyperparameters, the AI models employed in this study achieved remarkable accuracy in predicting the the INC’s victory in the election. The findings shed new light on the potential of cutting-edge transformer-based models in capturing and analyzing sentiment dynamics within the Indian political landscape. The implications of this research are far-reaching, providing invaluable insights to political parties for informed decision-making and strategic planning in preparation for the forthcoming 2024 Lok Sabha elections in the nation.
POLITICAL OPINION ANALYSIS IN SOCIAL NETWORKS: CASE OF TWITTER AND FACEBOOK dannyijwest
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.
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.
NEGOTIATING THE ANALOG MAINSTREAM WITH DIGITAL METHODS IN HAND VISIONS FROM T...Pertti Ahonen
Dr. Pertti Ahonen, Professor, Faculty of Social
Sciences, University of Helsinki, Finland,
pertti.ahonen@helsinki.fi
ESS Digital Sociology Mini-Conference,
Boston, March 17–20, 2016
Session 43, March 17, Thursday, 3:30 – 5:00 p.m.
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.
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.
• Created a data warehouse of Women Empowerment and Gender Gap, which gives better suggestions and more details for the prospective gender gap.
• Data collected from 6 different data sources ( both structured and unstructured data) using web scraping.
• Cleaned the dataset using R and automated script to load cleaned datasets and processed (ETL).
• Data was analyzed using Visual Studio and Microsoft SQL tools (SSIS, SSMS, SSAS) to create a data warehouse and deploy cube and finally visualized analysis using Tableau.
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.
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.
This paper presents the results of a new monitoring project of the US presidential elections with the aim of establishing computer-based tools to track in real time the popularity or awareness of candidates. The designed and developed innovative methods allow us to extract the frequency of queries sent to numerous search engines by US Internet users. Based on these data, this paper demonstrates that Trump was more frequently searched than the Democratic candidates, either Hillary Clinton in 2016 or Joe Biden in 2020. When analyzing the topics, it is observed that in 2020 the US users had shown a remarkable interest in two subjects, namely, Coronavirus and Jobs (unemployment). Interest for other topics such as Education or Healthcare were less pronounced while issues such as Immigration were given even less attention by users. Finally, some “flame” topics such as Black Lives Matter (2020) and Gun Control (2016) appear to be very popular for a few weeks before returning to a low level of interest. When analyzing tweets sent by candidates during the 2020 campaign, one can observe that Trump was focused mainly on Jobs and on Riots, announcing what would happen if Democrats took power. To these negative ads, Biden answered by putting forward moral values (e.g., love, honesty) and political symbols (e.g., democracy, rights) and by underlying the failure of the current administration in resolving the pandemic situation.
Analyzing Political Sentiment of Indic Languages with TransformersIJCI JOURNAL
This paper presents an analysis of sentiment on Twitter towards the Karnataka elections held in 2023, utilizing transformer-based models specifically designed for sentiment analysis in Indic languages. Through an innovative data collection approach involving a combination of novel methods of data augmentation, online data preceding the election was analyzed. The study focuses on sentiment classification, effectively distinguishing between positive, negative, and neutral posts, while specifically targeting the sentiment regarding the loss of the Bharatiya Janata Party (BJP) or the win of the Indian National Congress (INC). Leveraging high-performing transformer architectures, specifically IndicBERT, coupled with specifically fine-tuned hyperparameters, the AI models employed in this study achieved remarkable accuracy in predicting the the INC’s victory in the election. The findings shed new light on the potential of cutting-edge transformer-based models in capturing and analyzing sentiment dynamics within the Indian political landscape. The implications of this research are far-reaching, providing invaluable insights to political parties for informed decision-making and strategic planning in preparation for the forthcoming 2024 Lok Sabha elections in the nation.
IEEE PROJECTS 2016 - 2017
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It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Project Domain list 2016
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3. IEEE based on networking,
4. IEEE based on Image processing,
5. IEEE based on Multimedia,
6. IEEE based on Network security,
7. IEEE based on parallel and distributed systems
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
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1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
5. IOT Projects
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2. BCA/B.E(C.S)
3. B.Tech IT
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LOCATION-BASED SENTIMENT ANALYSIS OF 2019 NIGERIA PRESIDENTIAL ELECTION USING...kevig
Nigeria president Buhari defeated his closest rival Atiku Abubakar by over 3 million votes. He was issued a Certificate of Return and was sworn in on 29 May 2019. However, there were claims of widespread hoax by the opposition. The sentiment analysis captures the opinions of the masses over social media for global events. In this paper, we use 2019 Nigeria presidential election tweets to perform sentiment analysis through the application of a voting ensemble approach (VEA) in which the predictions from multiple techniques are combined to find the best polarity of a tweet (sentence). This is to determine public views on the 2019 Nigeria Presidential elections and compare them with actual election results. Our sentiment analysis experiment is focused on location-based viewpoints where we used Twitter location data. For this experiment, we live-streamed Nigeria 2019 election tweets via Twitter API to create tweets dataset of 583816 size, pre-processed the data, and applied VEA by utilizing three different Sentiment Classifiers to obtain the choicest polarity of a given tweet. Furthermore, we segmented our tweets dataset into Nigerian states and geopolitical zones, then plotted state-wise and geopolitical-wise user sentiments towards Buhari and Atiku and their political parties. The overall objective of the use of states/geopolitical zones is to evaluate the similarity between the sentiment of location-based tweets compared to actual election results. The results reveal that whereas there are election outcomes that coincide with the sentiment expressed on Twitter social media in most cases as shown by the polarity scores of different locations, there are also some election results where our location analysis similarity test failed.
LOCATION-BASED SENTIMENT ANALYSIS OF 2019 NIGERIA PRESIDENTIAL ELECTION USING...kevig
Nigeria president Buhari defeated his closest rival Atiku Abubakar by over 3 million votes. He was issued a
Certificate of Return and was sworn in on 29 May 2019. However, there were claims of widespread hoax
by the opposition. The sentiment analysis captures the opinions of the masses over social media for global
events. In this paper, we use 2019 Nigeria presidential election tweets to perform sentiment analysis
through the application of a voting ensemble approach (VEA) in which the predictions from multiple
techniques are combined to find the best polarity of a tweet (sentence). This is to determine public views on
the 2019 Nigeria Presidential elections and compare them with actual election results. Our sentiment
analysis experiment is focused on location-based viewpoints where we used Twitter location data. For this
experiment, we live-streamed Nigeria 2019 election tweets via Twitter API to create tweets dataset of
583816 size, pre-processed the data, and applied VEA by utilizing three different Sentiment Classifiers to
obtain the choicest polarity of a given tweet. Furthermore, we segmented our tweets dataset into Nigerian
states and geopolitical zones, then plotted state-wise and geopolitical-wise user sentiments towards Buhari
and Atiku and their political parties. The overall objective of the use of states/geopolitical zones is to
evaluate the similarity between the sentiment of location-based tweets compared to actual election results.
The results reveal that whereas there are election outcomes that coincide with the sentiment expressed on
Twitter social media in most cases as shown by the polarity scores of different locations, there are also
some election results where our location analysis similarity test failed.
This paper investigates the factors that influence how members of the Brazilian House of Representatives adopt Twitter as part of their political communication strategy. Our intention is to understand the main variables that lead legislators to invest time and resources in microblogging. This quantitative study examined all of the 457 official congressional accounts registered on Twitter. These accounts were monitored weekly between February 2012 and February 2013. After empirically exploring the data, this work reflects on the results thanks in part to an examination of the literature on the intersection of the Internet and politics. The results indicate correlations between the use of Twitter and attributes like age and occupation of leadership positions.
In the present times, social media is one such platform which has been useful in connecting the people throughout the world. Be it a personal interaction, a product promotion, an advertisement or a political campaign, social media has formed to be the best platform to connect to people globally. In this report the discussion will be focused on the how and why the social media has been used as the medium for political campaigns in offices. The importance of social media for political campaigns will be analyzed and discussed. Thus the research will be focused on the role of social media in political engagement. There will be analysis of how the new age media has increased the possibilities to the ideal situation for political campaign
Social Media and West Bengal Political Parties A Brief Analysisijtsrd
The rise of the social networking sites in the early 2000s, has led to the increase in the worlds networked population. Social media ensured that people have greater access to information, content and opportunities to engage in public sphere to undertake united action. Social media has entered into our daily lives and it influences people and organizations all over the world involving many actors regular citizens, social activists, non governmental organizations, telecommunications companies, software service providers, and also governments at large. Social media revolution in Indian politics is real and its impact can be assessed by General elections of 2014 and 2019. The General election of 2014 was regarded as the 1st social media election of India due to the ever increasing use of social media by political actors. Nevertheless, Social media has also impacted politics in all major states including West Bengal. No doubt social media is now being seriously considered by the West Bengal political parties as a mean to reach out to the electorate, but will it influence the 2021 Assembly Elections in the same way as in Obama’s Presidential elections This paper analyses the reach of West Bengal based parties in various social media platforms. Rajarshi Guha "Social Media and West Bengal Political Parties: A Brief Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38402.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/38402/social-media-and-west-bengal-political-parties-a-brief-analysis/rajarshi-guha
Understanding Inductive Bias in Machine LearningSUTEJAS
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PREDICTING ELECTION OUTCOME FROM SOCIAL MEDIA DATA
1. International Journal on Natural Language Computing (IJNLC) Vol.9, No.1, February 2020
DOI: 10.5121/ijnlc.2020.9103 21
PREDICTING ELECTION OUTCOME FROM SOCIAL
MEDIA DATA
Badhan Chandra Das1
and Md Musfique Anwar1
1
Department of Computer Science and Engineering, Jahangirnagar University,
Bangladesh.
badhan0951@gmail.com
manwar@juniv.edu
ABSTRACT
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.
KEYWORDS
Election Prediction, Social Network Analysis, Twitter, Regular Expression.
1. INTRODUCTION
Over the last few years, OSNs have become a reliable and effective medium of communication
and source of important information and news about daily life. OSNs and other micro-blogging
sites are such platforms which not only provides the option to be connected with their friends and
family members, colleagues, acquaintances, fans and their other well-wishers but it also offers the
facility to its users to share different contents like as pictures, texts, videos and others as well.
Approximately, 3.09 billion people are connected to various microblogging platforms including
Facebook, Twitter, Instagram, Google+, etc [15]. These vast number of people frequently use
these online platforms to share their opinion regarding the different affairs of their regular life. In
recent years, these OSNs have appeared one of the popular research fields among many
prominent researchers over the world [1]. Extracting meaningful information utilizing data of
those platforms is a notable task of their research. Twitter is such an online social platform where
anyone can write their thoughts about any issue in 140 characters and spread it among their
connections. Now it has become one of the greatest origins of news, with above 200 million
active users [2].
People share their opinion on various topics, for instance, business, sports, politics, entertainment,
literature and culture and so on. During election season concerned people often post tweets on
Twitter regarding different political parties including their success, failure, manifestos, positive,
negative aspects of their activities or about any specific leader of the corresponding party and also
several other stuff. The activity of voters on micro-blogs reflect their sentiment to any specific
2. International Journal on Natural Language Computing (IJNLC) Vol.9, No.1, February 2020
22
political group. Their tweets regarding any political party can be in favour of them or against
them. These micro-blogs often play a significant role not only in terms of understanding of public
intuition towards any party taking part in the election but also promoting those political groups.
Having an aim of winning the election, it is very essential to prepare a manifesto and set the
strategy to boost up promotional activities in accordance with the demand of mass people.
Oftentimes, the turnout of an election can be predicted approximately from these OSN posts by
analyzing the texts of that post on the basis of various relevant parameters. That is why
forecasting election outcomes is one of the major and trending tasks of the eminent researchers of
text processing. In this paper, we introduce a very simple and straightforward technique of
predicting election results based on tweets counts. We have taken into count those tweets, which
contain political party names, abbreviations of the party name, any of their leaders' names, the
moto of that party and all possible ways by which any corresponding party can be mentioned so
that the calculation of estimation of election produces higher accuracy. In brief, the basic
approaches of our contributions are as following:
We propose an easy and novel method of counting system by two-way extraction of the
virtual support from twitter mentions of political parties during the election season.
We perform a comprehensive experiment on a real-world Twitter dataset collected from
Kaggle [16] containing 1.8 million tweets collected throughout the election period.
We explain the techniques followed for recognizing the mention of any specific party in a
particular tweet and also present the comparison of the result of our predicted outcomes
with the original results of the election.
The remainder of this paper consists of as follows. We describe some relevant works already
done in this field in Section 2. In Section 3, we explain the formation of political parties. Section
4 will contain the methodologies used to perform the experiment and the computational equations
in order to calculate the election outcome. Section 5 will describe the validation of the
experimental results of forecasting as well as make some discussion concerning the outcomes of
our presented prophecy. Lastly, the paper puts an end with the conclusion in section 6.
2. RELATED WORKS
Among a bunch of research fields in Text Mining and Natural Language Processing (NLP),
forecasting election results, as well as analysis of the public concern on politics, have able to seek
the attention of renowned researchers over the world. Earlier works reported some notable works
in this field. Besides, political parties are always eager to discover public opinion about them,
hence, research on this specific arena has become really essential [3].
Basically, previous investigations have been explored this field from several points of view as
follows.
2.1. Analyzing Public Perspective from Social Media
Karami et. al. [4] reported an approach of extracting public opinion in order to explore the
discussion of the economic issue during the election period 2012 US presidential election on
Twitter. Another Twitter dataset was employed to study public opinion in the case of UK General
Election 2010 [5]. Studies have done on predicting the political preferences of users of twitter
networks by their online actions [7]. Apart from political issues public opinion mining tasks have
been done for several other purposes e.g. decision making for business strategy [17] [18], Kim et.
al. have shown how reviews of social media can be a determinant of improvement of restaurants’
3. International Journal on Natural Language Computing (IJNLC) Vol.9, No.1, February 2020
23
performance [19]. Gadekallu et. al. worked on an IMDB data and showed applications of
sentiment analysis e.g. determining marketing strategy, improving campaign success, etc [20].
2.2. Election Prediction Using Sentiment Analysis
Sentiment analysis is one of the popular manners of predicting elections among researchers. This
approach has been used by the researchers in order to predict elections of several countries. For
instance, Indonesia Presidential election [9], The Nigeria Presidential Election 2019 [8], French
Election [13]. In accordance with that, Heredia et. al. [3] proposed a prediction technique
incorporating location-based sentiment analysis of U.S. Presidential Election 2016. Oyebode et.
al. [8] claim a new lexicon-based approach named VADER-EXT which outperforms two other
sentiment lexicon library-based approaches known as VADER and Textblob.
2.3. Mixed Approaches
Karami et. al. [4] combined both sentiment analysis and topic modeling approach to find out
meaningful information from tweets. Heredia et. al. [6] reported an approach of predicting
election of a mixed approach which includes both polling and sentiment analysis. Unankard et. al.
[10] introduced a technique of forecasting the election result by associating both sub-event and
sentiment analysis. Sanders et. al. [12] in 2016, used demographic information to predict Dutch
election results, again, in 2019, asserted that, demographic statistics do not really help in
forecasting the elections and proposed a simple tweet count method for doing so [11]. Sanga et.
al. proposed a Bayesian network model on Indian general election, 2019 [21].
All the approaches of forecasting the turnout of election cited above are a bit complex and time
consuming since they need to employ several lexicon libraries and probabilistic theorem. Our
proposed method consists of a very simple and effective procedure for performing the above task.
3. FORMATION OF AUSTRALIAN POLITICAL PARTIES
In this paper, we are about to report an investigation on a dataset of Australian federal election
2019 which took place on 18th May 2019. Several parties participated in the election in order to
form the government. A group of parties often make an alliance between them to take part in
elections as well as forming the government and for working together later. Making such
collaboration between political groups like this is generally known as coalition. The Australian
federal election has also seen such a coalition. Table 1. shows the names of the major parties,
coalitions, and their leader names.
Table 1. Major Political Parties, their Coalitions and the leaders
4. PROPOSED METHODOLOGY
4. International Journal on Natural Language Computing (IJNLC) Vol.9, No.1, February 2020
24
4.1. Counting Procedure of Tweets
A huge amount of Twitter dataset which contains around 1.8 Million tweets collected from 10
May 2019 to 20 May 2019. As the election date comes near, the frequency of posting tweets
regarding election increases rapidly. A political party in a tweet can be mentioned in various
ways. Sometimes a party name may be a bit large, besides, writing the full name with the proper
spelling can be daunting as well as time-consuming. This is why referring the full name of any
political party in a tweet is not convenient always. Moreover, it is not mandatory to mention the
full official name of any organization on such a casual online platform like Twitter. Similarly,
from our observation of the dataset, we noticed that most of the users who posted about any
political party on Twitter did not write the complete formal name of the party always. People
commonly used acronyms, a portion of the name by which a party can be recognized, the name of
the leader of the corresponding party or any philosophy, ideology or doctrine of that respective
political group to espouse them. So, determining the tweets from a huge amount of data contains
names of the specific political wing, needs a generalized method that can extract only those data
pertaining to analogous information regarding that party.
Considering all those points above, we have chosen the following approaches to construct the
counting process more efficient.
4.1.1. Regular Expressions for Party Mentions
Regular Expression is such a technique to find any specific or any conditional pattern, characters,
numbers or symbols in a text. Utilizing the above technique to identify the party names in the
tweets by taking almost all possible cases into account through which a party can be cited in an
instance of a tweet. For example, the regular expression for the first instance of Table 2 will
detect the mention of the party Liberal National Coalition as Liberal National Coalition, LNC,
L.N.C., Liberal Party and all other possible cases. The Regular Expressions we employed in order
to find party mentions shown in the following table.
Table 2. Regular Expressions to find party mentions in tweets
4.1.2. Search for Specific Relevant Keywords
5. International Journal on Natural Language Computing (IJNLC) Vol.9, No.1, February 2020
25
Sometimes it is seen that mass public quote the popular morals, taglines, ideologies or the name
of the leader or the founder of any political group to show their enthusiasm towards the party. As
a result, any generalized strategy is unable to recognize these types of implicit mentions in the
tweets. Taking this issue into account, in order to resolve this problem, we manually searched
according to the above-mentioned keys in the tweets. We engaged str.contains(‘’) function
offered by Pandas library in python version 3.7.
After having outputs from both operations, we aggregate both of the outputs to perform the
computational experiments to get the prediction result.
4.2. Parameters Consideration
i. We have ignored the location, occupation and other demographic features of twitter users
since this paper introduces a very simple approach to forecast election results.
ii. Removal of redundant instances and elimination of stopwords has been done at the early
stage of the experiment.
iii. Retweets and comments were not taken into counts because retweeting not denote the
support for the original tweet post every time. Along with this, URLs in a tweet don't
hold much information in terms of advocation for any particular group. For this reason,
URLs also get outweighed our attention to be counted as a parameter.
iv. As we stated earlier, the tendency of tweeting regarding election rises considerably when
the date of election comes closer. Figure 1. demonstrates this statement graphically,
where X-axis denotes the date of the month of May 2019 and the Y-axis indicates the
number of tweets.
v. Our consideration was to measure the mentions for the top three parties received 96.7%
votes in the actual election held on 18th May 2019 in Australia.
Figure 1: Graphical representation of party mentions during election period
4.3. Average Tweets Count and Percentage
6. International Journal on Natural Language Computing (IJNLC) Vol.9, No.1, February 2020
26
Let MC(X)(i) be the counts for mentions on behalf of party X on the day i and N is the sum of the
day counts. Un is the number of unique users tweets in favor of party X, then, the average tweet
counts per user mention among all the tweets for party X is,
Where ATCP(X) denotes the average tweets counts per user mention in the tweet for party X,
If the number of political parties is P, then the party X acquires the percentage of mentions
denoted by PREC(X) shown in Eq. 2. Where the numerator is computed after applying ceiling
function on the values yielding from Eq. 1 and denominator represents the summation of all
average tweet counts for P parties. Ceiling function of x is the value gives the smallest integer
which is greater than or equal x. For example, Ceil(3.5) will provide 4 as output.
5. EXPERIMENTAL RESULT AND DISCUSSION
The evaluation of our proposed framework has been conducted by two of the most common and
well recognized metrics named Mean Absolute Error (MAE) and Root Mean Squared Error
(RMSE), which are frequently used to measure accuracy for continuous variables.
Mean Absolute Error (AE) it is a term which determines the measurement of flaws in the
assessment compared with original values. It is also known as Absolute Accuracy Error (AAE).
MAE is the average of all AEs. In our proposed context, PRECel(i) and PRECmc(i) refer to the
percentage of vote earned by party i in the election and the percentage of the mention counts for
party i among the tweets through our suggested system.
Root Mean Squared Error (RMSE) is known as a measurement of standard deviation which
denotes how much spread out the predicted value is from the real value. Usually, this procedure is
appropriate for determining the standard deviation of the residuals. Residuals are referred to the
prediction errors which means how distant the regression line is from the actual data points. Eq. 4
shows how RMSE is calculated.
The implicit indication behind the above discussion is that the less the value of these two metrics
the greater the accuracy of the framework will be.
The comparison between the percentages of our predicted outcome and the real election result
represented graphically in Figure 2. We can see clearly that, the flaw of our investigation is very
little for the top two parties according to the real turnout of the election that took place on 18th of
may [14].
7. International Journal on Natural Language Computing (IJNLC) Vol.9, No.1, February 2020
27
Figure 2: The comparison between the percentage of forecast from tweets and votes of actual election for
three major political parties
The correctness of any system grows higher as the value of MAE and RMSE drops and the value
of RMSE will always be greater than the value of MAE. Our experimental result follows these
ground rules of the two above mentioned accuracy metrics. The value of the MAE and RMSE of
the experimental result is shown in Table 3.
Table 3. The Result of Two Evaluation Metrics with respect to the main election
6. CONCLUSION
In this paper, we meticulously examined a dataset of Australian federal election 2019 and made a
forecast of the percentage of votes gained by each party. The counting procedure followed two
useful manners to extract party mentions in an instance of a tweet. The obtained outcome yields
our proposed approach closely matches the actual percentage count of the election and gives a
little error. The evaluation of the prediction outcomes has executed by two most well-known
accuracy metrics.
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AUTHORS
Badhan Chandra Das is an M.Sc. student of Computer Science and Engineering,
Jahangirnagar University, Savar, Dhaka, Bangladesh. He has completed his B.Sc.
also in Computer Science and Engineering, from Jahangirnagar University in 2017.
His research is concentrated on Data Mining, Social Network Analysis, Natural
Language Processing and developing Machine Learning models for Intelligent tools.
He has several publications on international conferences. Currently, he is working on
a funded research project of University Grants Commission (UGC) of Bangladesh.
Md Musfique Anwar has awarded PhD degree from the Department of Computer
Science and Software Engineering, Faculty of Science, Engineering and Technology
of Swinburne University of Technology, Melbourne, Australia in 2018. He has
received M.Sc. degree from the Department of Intelligence Science and Technology,
Graduate School of Informatics of Kyoto University, Japan in 2013 and B.Sc. degree
in Computer Science and Engineering from Jahangirnagar University, Savar, Dhaka,
Bangladesh in 2006. Since 2008, he is a faculty member having current designation
Associate Professor in the Department of Computer Science and Engineering of Jahangirnagar University,
Savar, Dhaka, Bangladesh. Currently, his research focuses on Data Mining, Social Network Analysis,
Natural Language Processing and Software Engineering. He achieved Best Student Paper award in 29th
Australasian Database Conference (ADC) in 2018, Best Poster award in 26th Australasian Database
Conference (ADC) in 2015 and Best Poster Paper award in International Workshop on Computer Vision
and Intelligent Systems-2019 (IWCVIS2019) in 2019.