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IRJET - Election Result Prediction using Sentiment Analysis

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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 02 | Feb 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2855
Election result prediction using Sentiment Analysis
Madhura Shirodkar1, Sayali Nimbalkar2, Ashlesha Ingole3, Pragati Mishra4, Sandeep Sahu5
1Professor, Dept. of Electronics and Telecommunication, Xavier Institute of Engineering, Mumbai,
Maharashtra, India
2,3,4,5Student, Dept. of Electronics and Telecommunication, Xavier Institute of Engineering, Mumbai,
Maharashtra, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In the recent year, social media hasprovidedend
users a powerful platform to voice their opinions. Opinion of
people matters a lot to analyze how the propagation of
information impacts the lives in a large-scale network like
Twitter. Businesses need to identify the polarity of these
opinions in order to understand user orientation and thereby
make smarter decisions. One such application is in the field of
politics, where political entities need to understand public
opinion to determine their campaigning strategy. Twitter is
indeed used extensively for political deliberation. We findthat
the mere number of messages mentioning a party reflects the
election result. This data is used to predict outcome ofelection
by using sentiment analysis. Sentiment analysis of the tweets
determine the polarity and inclination of vast population
towards specific topic, item or entity. Popular text
classification algorithms like Naive Bayes and SVM are
Supervised Learning Algorithmswhichrequireatraining data
set to perform Sentiment analysis. These algorithms are
utilized to build classifier and classified the test data as
positive, negative and neutral. A two stage framework can be
formed to create a training data from the mined Twitter data
and to propose a scalable machine learning model to predict
the election result.
Key Words: Social Media, Twitter, Politics, Sentiment
Analysis, Naive Bayes, Support Vector Machine
1. INTRODUCTION
An election is a most important part in the democracy. It is
the instrument of democracy wherethevoterscommunicate
with the representatives. Due to their important role in
politics, there has been a big interest in predicting an
election outcome. Traditional polls are too costly and still
accuracy won’t be achieved. To overcomethisproblemsocial
media has been used as it is easy and freely available. Social
media sites have become valuable sources for opinion
mining because people post everythingrightfromthedetails
of their life to the products and services they use, to give
opinions about the current issues such as political and
religious views. Millions of messages are being posted every
day on popular social media sites such as Twitter,Instagram
and Facebook. Twitter is an online social networkingservice
that enables users to send and read short 240-character
messages called ”tweets”. Along with the short messages,
users can use the hashtags before a relevant keyword or
phrase in their Tweet to categorize those Tweets and help
them show more easily in Twitter Search. The use of hash
tags makes the problem of textclassificationrelativelyeasier
since the hash tag itself can convey an emotion or opinion.
Currently around 6500 tweets are published per second,
which results in approximately 561.6milliontweetsper day.
Facebook is the most popular and well known Social
Networking Service (SNS) all over the world. According to
reports 86.3 percent people reported using the Internet in
the previous six months. So Facebook is also one of the good
sources among various social media’s available to be
considered as a data source. In June 2016, the number of
active Instagram users, those who use the application on a
weekly basis, surpassed the number of active users on
Twitter reaching a total of more than 500 million, of these
some 300 million use their accounts at least once every day.
This total has reached even greater heights in 2017 with the
social media company boasting a total of 700 million active
users, making Instagram the second most widelyusedsocial
media platform after Facebook. Since Instagram attracts in
surplus of 700 million users worldwide, allowing them to
share and promote material at little cost, it would seem
rational for political parties to want to take advantageofthis
communication tool. This is an interestingresearcharea that
combines politics and social media which both concern
today’s society.
2. LITERATURE REVIEW
This section summarizes some of the scholarly articles and
research works in the field of Machine Learning and data
mining to analyze sentiments on Twitter and preparing
prediction model for various applications. The use of social
media in last few decades have been helpful in determining
people’s attitude with respect to specific topics or events, a
wide research interest in natural language processing and
determining the sentiments based on it. In [1], the collected
tweets are analyzed using lexicon based approach to
determine the sentiments of public and a comparison is
made among the candidates over the typeofsentiment.Also,
a word cloud is plotted representing most frequently
appearing words in the tweets. Sentiment analysis on social
media data has been done by authors of [2] as it is an
effective tool to monitor user preferences and inclination.
Popular text classification algorithms like Naive Bayes and
SVM are Supervised Learning Algorithms which require a
training data set to perform Sentiment analysis. Corpus
collection, linguistic analysis and Training a classifier was
performed step by step in [3]. Corpus is a collection of
written texts. They collected a corpus of 300,000 text posts
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 02 | Feb 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2856
from Twitter then evenly split into three sets of texts:
Positive, Negative and Neutral. It is basedontheNaiveBayes
classifier that uses N-gram and POS-tags as features. With
respect to [4], the mere number of messages reflects the
election result and even comes close to traditional election
polls. Using LIWC text analysis software, they conducted
analysis of over 100,000 messages containing a reference to
either a political party or a politician. Theirresultshowsthat
Twitter is indeed used extensively for political deliberation
and mere number of messages mentioning a party reflects
the election result. Utilization of Dictionary Based, Naive
Bayes and SVM algorithm to build the classifier was done by
[5] and classified the test data as positive, negative and
neutral. They identified the sentiment of Twitter users
towards each of the considered Indian political parties. The
results of the analysis for Naive Bayes was the BJP (Bhartiya
Janta Party), for SVM it was the BJP (Bhartiya Janta Party)
and for the Dictionary Approach it was the Indian National
Congress. In [6], authors have used Lexicon based approach
with machine learning tofindemotionsintweetsandpredict
sentiment score. The research also showed that lexicon
based sentiment analysis improves thepredictionresult, but
the improvements also vary in different states. The authors
of [7], used fast and in memory computation framework
’Apache Spark’ to extract live tweets and perform sentiment
analysis. This paper provides a method for analyzing
sentiment score in noisy twitter streams and reports on the
design of a sentiment analysis by classifying user’s
perception via tweets into positive and negative. The result
of Indonesian Election waspredicted byauthorsofpaper [8].
Indonesian people had not elected a president until 2004, so
Presidential candidate in Indonesia become a hot and
interesting conversation among Indonesian citizen, and
many of them expressed it through social media. In this
research, the authors focused on tweets related to 2019
Presidential election with top keywords. Among Jokowi and
Probowo result is producedbyusingR languageshowed that
Jokowi leads the election.
3. PROPOSED METHOD
Researchers use a different approach for election result
prediction. There are researchers who tried to discover the
political or ideology preference of a user, then relate ittothe
election and there are others who used tweets and polling
system related to the upcoming electionandfiguredoutvote
preference of the user. Some of them have used polling
system which has less number of opinions and could not be
used for prediction of accurate result. Most of the
researchers have used Twitter as theirsourceofdata butnot
everyone is active on Twitter. To overcomethesedrawbacks
considering social media sites such as Facebook, Instagram
and Twitter simultaneously can be an advantage. Twitter
allows its users to retrieve tweets by using twitter API.
Twitter API is simply a set of URLs that take parameters. The
URLs let you access many featuresof Twitter,suchasposting
a tweet or finding tweets that containrelatedinformation on
a specific topic. Facebook has complicated, highly granular,
poorly-documented privacy settings. Working with it
requires trial and error and a ton of error handling code. It
has received less attention because it is complicated and
different from other social media APIs. Data extraction from
Instagram is also done with the help of various API. Panoply
has recently integratedtheir data warehousewithInstagram
API to collect data. After extraction most important process
is data preprocessing. The basic flow of this project is as
shown below,
Fig -1: Proposed Model
As explained earlier, Data is retrieved by using differentAPI.
Next stage is preprocessing. In this stage, special characters
like ’@’ and URLs can be stripped off to overcome noise. One
of the most important goals of preprocessing is to enhance
the quality of the data by removing noise. It is a technique
which is used to transform the raw data in a useful and
efficient format. After Preprocessing, applying SVM and
Naive Bayes Algorithm to classify them. A Support Vector
Machine (SVM) is a supervised machine learning algorithm
that can be employed for both classification and regression
purposes. SVMs are based on the idea of finding a
hyperplane that best divides a datasetintotwoclasses.Naive
bayes is a classification technique based on Bayes’ Theorem
with an assumption of independence among predictors. In
simple terms, a Naive Bayes classifier assumes that the
presence of a particular feature in a class is unrelated to the
presence of any other feature. These algorithms will classify
the data according to related party. Sentiment analysis can
be done on classified data to determine polarity of the word.
Researches have done sentiment analysis on words but by
applying sentiment analysis on whole sentencemayprovide
better result. The classified data is then given polarity
depending upon the sentiment whether it is positive,
negative or neutral for each existing party. The result can be
represented in the form of pie chart, graph or tables. The
comparison between various parties can bedone byplotting
graphical figures.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 02 | Feb 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2857
4. CONCLUSION
Naive Bayes and SVM are supervised learning algorithms
used to classify data according to parties. Most of the
researches have extracted only twitter data but we can also
use other social media sites like Facebook and Instagram to
fetch data. The data used so far is in the form of words and
sentiment analysis is applied on it to determine the polarity
of the word. But applying sentiment analysis on sentences it
may provide better results than onwords.Final resultcanbe
obtained by comparing Sentiment Percent of various
political parties obtained by using above algorithms.
REFERENCES
[1] Ms. Farha Nausheen et al., “Sentiment Analysis to
Predict Election Results Using Python”, Proceedings of
the Second International Conference on Inventive
Systems and Control (ICISC 2018).
[2] Jyoti Ramteke et al., “Election Result Prediction Using
Twitter Sentiment Analysis”, 2016 International
Conference on Inventive Computation
Technologies(ICICT).
[3] Alexander Pak, et al., “Twitter as a Corpus for Sentiment
Analysis and Opinion Mining”, Proceedings of the
Universit e de Paris-Sud, Laboratoire LIMSI-CNRS.K.
Elissa, “Title of paper if known,” unpublished.
[4] Andranik Tumasjan et al., “Predicting Elections with
Twitter: What 140 Characters Reveal about Political
Sentiment”, Proceedings of the Fourth International
AAAI Conference on Weblogs and Social Media.
[5] Parul Sharma et al., “Prediction of Indian Election Using
Sentiment Analysis on Hindi Twitter”, 2016 IEEE
International Conference on Big Data (Big Data).
[6] Pritee Salunkhe et al.,“TwitterBasedElectionPrediction
and Analysis”, International Research Journal of
Engineering and Technology (IRJET) Volume: 04 Issue:
10 | Oct -2017.
[7] Amandeep Kaur et al., “Sentiment Analysis On Twitter
Using Apache Spark”, Carleton University Project
Report.
[8] Widodo Budiharto et al., “Prediction and analysis of
Indonesia Presidential election from Twitter using
sentiment analysis”, J Big Data (2018) 5:51.
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IRJET - Election Result Prediction using Sentiment Analysis

  • 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 02 | Feb 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2855 Election result prediction using Sentiment Analysis Madhura Shirodkar1, Sayali Nimbalkar2, Ashlesha Ingole3, Pragati Mishra4, Sandeep Sahu5 1Professor, Dept. of Electronics and Telecommunication, Xavier Institute of Engineering, Mumbai, Maharashtra, India 2,3,4,5Student, Dept. of Electronics and Telecommunication, Xavier Institute of Engineering, Mumbai, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In the recent year, social media hasprovidedend users a powerful platform to voice their opinions. Opinion of people matters a lot to analyze how the propagation of information impacts the lives in a large-scale network like Twitter. Businesses need to identify the polarity of these opinions in order to understand user orientation and thereby make smarter decisions. One such application is in the field of politics, where political entities need to understand public opinion to determine their campaigning strategy. Twitter is indeed used extensively for political deliberation. We findthat the mere number of messages mentioning a party reflects the election result. This data is used to predict outcome ofelection by using sentiment analysis. Sentiment analysis of the tweets determine the polarity and inclination of vast population towards specific topic, item or entity. Popular text classification algorithms like Naive Bayes and SVM are Supervised Learning Algorithmswhichrequireatraining data set to perform Sentiment analysis. These algorithms are utilized to build classifier and classified the test data as positive, negative and neutral. A two stage framework can be formed to create a training data from the mined Twitter data and to propose a scalable machine learning model to predict the election result. Key Words: Social Media, Twitter, Politics, Sentiment Analysis, Naive Bayes, Support Vector Machine 1. INTRODUCTION An election is a most important part in the democracy. It is the instrument of democracy wherethevoterscommunicate with the representatives. Due to their important role in politics, there has been a big interest in predicting an election outcome. Traditional polls are too costly and still accuracy won’t be achieved. To overcomethisproblemsocial media has been used as it is easy and freely available. Social media sites have become valuable sources for opinion mining because people post everythingrightfromthedetails of their life to the products and services they use, to give opinions about the current issues such as political and religious views. Millions of messages are being posted every day on popular social media sites such as Twitter,Instagram and Facebook. Twitter is an online social networkingservice that enables users to send and read short 240-character messages called ”tweets”. Along with the short messages, users can use the hashtags before a relevant keyword or phrase in their Tweet to categorize those Tweets and help them show more easily in Twitter Search. The use of hash tags makes the problem of textclassificationrelativelyeasier since the hash tag itself can convey an emotion or opinion. Currently around 6500 tweets are published per second, which results in approximately 561.6milliontweetsper day. Facebook is the most popular and well known Social Networking Service (SNS) all over the world. According to reports 86.3 percent people reported using the Internet in the previous six months. So Facebook is also one of the good sources among various social media’s available to be considered as a data source. In June 2016, the number of active Instagram users, those who use the application on a weekly basis, surpassed the number of active users on Twitter reaching a total of more than 500 million, of these some 300 million use their accounts at least once every day. This total has reached even greater heights in 2017 with the social media company boasting a total of 700 million active users, making Instagram the second most widelyusedsocial media platform after Facebook. Since Instagram attracts in surplus of 700 million users worldwide, allowing them to share and promote material at little cost, it would seem rational for political parties to want to take advantageofthis communication tool. This is an interestingresearcharea that combines politics and social media which both concern today’s society. 2. LITERATURE REVIEW This section summarizes some of the scholarly articles and research works in the field of Machine Learning and data mining to analyze sentiments on Twitter and preparing prediction model for various applications. The use of social media in last few decades have been helpful in determining people’s attitude with respect to specific topics or events, a wide research interest in natural language processing and determining the sentiments based on it. In [1], the collected tweets are analyzed using lexicon based approach to determine the sentiments of public and a comparison is made among the candidates over the typeofsentiment.Also, a word cloud is plotted representing most frequently appearing words in the tweets. Sentiment analysis on social media data has been done by authors of [2] as it is an effective tool to monitor user preferences and inclination. Popular text classification algorithms like Naive Bayes and SVM are Supervised Learning Algorithms which require a training data set to perform Sentiment analysis. Corpus collection, linguistic analysis and Training a classifier was performed step by step in [3]. Corpus is a collection of written texts. They collected a corpus of 300,000 text posts
  • 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 02 | Feb 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2856 from Twitter then evenly split into three sets of texts: Positive, Negative and Neutral. It is basedontheNaiveBayes classifier that uses N-gram and POS-tags as features. With respect to [4], the mere number of messages reflects the election result and even comes close to traditional election polls. Using LIWC text analysis software, they conducted analysis of over 100,000 messages containing a reference to either a political party or a politician. Theirresultshowsthat Twitter is indeed used extensively for political deliberation and mere number of messages mentioning a party reflects the election result. Utilization of Dictionary Based, Naive Bayes and SVM algorithm to build the classifier was done by [5] and classified the test data as positive, negative and neutral. They identified the sentiment of Twitter users towards each of the considered Indian political parties. The results of the analysis for Naive Bayes was the BJP (Bhartiya Janta Party), for SVM it was the BJP (Bhartiya Janta Party) and for the Dictionary Approach it was the Indian National Congress. In [6], authors have used Lexicon based approach with machine learning tofindemotionsintweetsandpredict sentiment score. The research also showed that lexicon based sentiment analysis improves thepredictionresult, but the improvements also vary in different states. The authors of [7], used fast and in memory computation framework ’Apache Spark’ to extract live tweets and perform sentiment analysis. This paper provides a method for analyzing sentiment score in noisy twitter streams and reports on the design of a sentiment analysis by classifying user’s perception via tweets into positive and negative. The result of Indonesian Election waspredicted byauthorsofpaper [8]. Indonesian people had not elected a president until 2004, so Presidential candidate in Indonesia become a hot and interesting conversation among Indonesian citizen, and many of them expressed it through social media. In this research, the authors focused on tweets related to 2019 Presidential election with top keywords. Among Jokowi and Probowo result is producedbyusingR languageshowed that Jokowi leads the election. 3. PROPOSED METHOD Researchers use a different approach for election result prediction. There are researchers who tried to discover the political or ideology preference of a user, then relate ittothe election and there are others who used tweets and polling system related to the upcoming electionandfiguredoutvote preference of the user. Some of them have used polling system which has less number of opinions and could not be used for prediction of accurate result. Most of the researchers have used Twitter as theirsourceofdata butnot everyone is active on Twitter. To overcomethesedrawbacks considering social media sites such as Facebook, Instagram and Twitter simultaneously can be an advantage. Twitter allows its users to retrieve tweets by using twitter API. Twitter API is simply a set of URLs that take parameters. The URLs let you access many featuresof Twitter,suchasposting a tweet or finding tweets that containrelatedinformation on a specific topic. Facebook has complicated, highly granular, poorly-documented privacy settings. Working with it requires trial and error and a ton of error handling code. It has received less attention because it is complicated and different from other social media APIs. Data extraction from Instagram is also done with the help of various API. Panoply has recently integratedtheir data warehousewithInstagram API to collect data. After extraction most important process is data preprocessing. The basic flow of this project is as shown below, Fig -1: Proposed Model As explained earlier, Data is retrieved by using differentAPI. Next stage is preprocessing. In this stage, special characters like ’@’ and URLs can be stripped off to overcome noise. One of the most important goals of preprocessing is to enhance the quality of the data by removing noise. It is a technique which is used to transform the raw data in a useful and efficient format. After Preprocessing, applying SVM and Naive Bayes Algorithm to classify them. A Support Vector Machine (SVM) is a supervised machine learning algorithm that can be employed for both classification and regression purposes. SVMs are based on the idea of finding a hyperplane that best divides a datasetintotwoclasses.Naive bayes is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. These algorithms will classify the data according to related party. Sentiment analysis can be done on classified data to determine polarity of the word. Researches have done sentiment analysis on words but by applying sentiment analysis on whole sentencemayprovide better result. The classified data is then given polarity depending upon the sentiment whether it is positive, negative or neutral for each existing party. The result can be represented in the form of pie chart, graph or tables. The comparison between various parties can bedone byplotting graphical figures.
  • 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 02 | Feb 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2857 4. CONCLUSION Naive Bayes and SVM are supervised learning algorithms used to classify data according to parties. Most of the researches have extracted only twitter data but we can also use other social media sites like Facebook and Instagram to fetch data. The data used so far is in the form of words and sentiment analysis is applied on it to determine the polarity of the word. But applying sentiment analysis on sentences it may provide better results than onwords.Final resultcanbe obtained by comparing Sentiment Percent of various political parties obtained by using above algorithms. REFERENCES [1] Ms. Farha Nausheen et al., “Sentiment Analysis to Predict Election Results Using Python”, Proceedings of the Second International Conference on Inventive Systems and Control (ICISC 2018). [2] Jyoti Ramteke et al., “Election Result Prediction Using Twitter Sentiment Analysis”, 2016 International Conference on Inventive Computation Technologies(ICICT). [3] Alexander Pak, et al., “Twitter as a Corpus for Sentiment Analysis and Opinion Mining”, Proceedings of the Universit e de Paris-Sud, Laboratoire LIMSI-CNRS.K. Elissa, “Title of paper if known,” unpublished. [4] Andranik Tumasjan et al., “Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment”, Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media. [5] Parul Sharma et al., “Prediction of Indian Election Using Sentiment Analysis on Hindi Twitter”, 2016 IEEE International Conference on Big Data (Big Data). [6] Pritee Salunkhe et al.,“TwitterBasedElectionPrediction and Analysis”, International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 10 | Oct -2017. [7] Amandeep Kaur et al., “Sentiment Analysis On Twitter Using Apache Spark”, Carleton University Project Report. [8] Widodo Budiharto et al., “Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis”, J Big Data (2018) 5:51.