Real-Time Sentiment Analysis of 2019 Election Tweets using Word2vec and Random Forest Model

MSR Hitesh; Vedhosi Vaibhav; Y.J Abhishek Kalki; Suraj Harsha Kamtam; Santoshi Kumari

doi:10.1109/ICCT46177.2019.8969049

Real-Time Sentiment Analysis of 2019 Election Tweets using Word2vec and Random Forest Model

MSR Hitesh
, Vedhosi Vaibhav
, Y.J Abhishek Kalki
, Suraj Harsha Kamtam
, Santoshi Kumari

M S Ramaiah University of Applied Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding

44 Citations (Scopus)

158 Downloads (Pure)

Abstract

Sentiment analysis of social media data consists of attitudes, assessments, and emotions which can be considered a way human think. Understanding and classifying the large collection of documents into positive and negative aspects are a very difficult task. Social networks such as Twitter, Facebook, and Instagram provide a platform in order to gather information about people's sentiments and opinions. Considering the fact that people spend hours daily on social media and share their opinion on various different topics helps us analyze sentiments better. More and more companies are using social media tools to provide various services and interact with customers. Sentiment Analysis (SA) classifies the polarity of given tweets to positive and negative tweets in order to understand the sentiments of the public. This paper aims to perform sentiment analysis of real-time 2019 election twitter data using the feature selection model word2vec and the machine learning algorithm random forest for sentiment classification. Word2vec with Random Forest improves the accuracy of sentiment analysis significantly compared to traditional methods such as BOW and TF-IDF. Word2vec improves the quality of features by considering contextual semantics of words in a text hence improving the accuracy of machine learning and sentiment analysis.

Original language	English
Title of host publication	2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)
Publisher	IEEE
Pages	146-151
Number of pages	6
Volume	1
ISBN (Electronic)	978-1-7281-1711-9
ISBN (Print)	978-1-7281-1712-6
DOIs	https://doi.org/10.1109/ICCT46177.2019.8969049
Publication status	Published - 28 Jan 2020
Externally published	Yes
Event	2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT) - Manipal University Jaipur, Jaipur, India Duration: 28 Sept 2019 → 29 Sept 2019 https://www.easychair.org/cfp/ICCT19

Conference

Conference	2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)
Abbreviated title	ICCT 2019
Country/Territory	India
City	Jaipur
Period	28/09/19 → 29/09/19
Internet address	https://www.easychair.org/cfp/ICCT19

Bibliographical note

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Keywords

Sentiment Analysis
Word2Vec
Random Forest
Twitter data analysis
TF-IDF
BOW

Access to Document

10.1109/ICCT46177.2019.8969049

Kamtam2020AAMAccepted author manuscript, 1.12 MB

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8969049

Cite this

Real-Time Sentiment Analysis of 2019 Election Tweets using Word2vec and Random Forest Model. / Hitesh, MSR; Vaibhav, Vedhosi; Kalki, Y.J Abhishek et al.
2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT). Vol. 1 IEEE, 2020. p. 146-151 8969049.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding

Hitesh, MSR, Vaibhav, V, Kalki, YJA, Kamtam, SH & Kumari, S 2020, Real-Time Sentiment Analysis of 2019 Election Tweets using Word2vec and Random Forest Model. in 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT). vol. 1, 8969049, IEEE, pp. 146-151, 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur, India, 28/09/19. https://doi.org/10.1109/ICCT46177.2019.8969049

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abstract = "Sentiment analysis of social media data consists of attitudes, assessments, and emotions which can be considered a way human think. Understanding and classifying the large collection of documents into positive and negative aspects are a very difficult task. Social networks such as Twitter, Facebook, and Instagram provide a platform in order to gather information about people's sentiments and opinions. Considering the fact that people spend hours daily on social media and share their opinion on various different topics helps us analyze sentiments better. More and more companies are using social media tools to provide various services and interact with customers. Sentiment Analysis (SA) classifies the polarity of given tweets to positive and negative tweets in order to understand the sentiments of the public. This paper aims to perform sentiment analysis of real-time 2019 election twitter data using the feature selection model word2vec and the machine learning algorithm random forest for sentiment classification. Word2vec with Random Forest improves the accuracy of sentiment analysis significantly compared to traditional methods such as BOW and TF-IDF. Word2vec improves the quality of features by considering contextual semantics of words in a text hence improving the accuracy of machine learning and sentiment analysis.",

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author = "MSR Hitesh and Vedhosi Vaibhav and Kalki, \{Y.J Abhishek\} and Kamtam, \{Suraj Harsha\} and Santoshi Kumari",

note = "{\textcopyright} 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Copyright {\textcopyright} and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. This document is the author{\textquoteright}s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it. ; 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT), ICCT 2019 ; Conference date: 28-09-2019 Through 29-09-2019",

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AU - Kalki, Y.J Abhishek

AU - Kamtam, Suraj Harsha

AU - Kumari, Santoshi

N1 - © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

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N2 - Sentiment analysis of social media data consists of attitudes, assessments, and emotions which can be considered a way human think. Understanding and classifying the large collection of documents into positive and negative aspects are a very difficult task. Social networks such as Twitter, Facebook, and Instagram provide a platform in order to gather information about people's sentiments and opinions. Considering the fact that people spend hours daily on social media and share their opinion on various different topics helps us analyze sentiments better. More and more companies are using social media tools to provide various services and interact with customers. Sentiment Analysis (SA) classifies the polarity of given tweets to positive and negative tweets in order to understand the sentiments of the public. This paper aims to perform sentiment analysis of real-time 2019 election twitter data using the feature selection model word2vec and the machine learning algorithm random forest for sentiment classification. Word2vec with Random Forest improves the accuracy of sentiment analysis significantly compared to traditional methods such as BOW and TF-IDF. Word2vec improves the quality of features by considering contextual semantics of words in a text hence improving the accuracy of machine learning and sentiment analysis.

AB - Sentiment analysis of social media data consists of attitudes, assessments, and emotions which can be considered a way human think. Understanding and classifying the large collection of documents into positive and negative aspects are a very difficult task. Social networks such as Twitter, Facebook, and Instagram provide a platform in order to gather information about people's sentiments and opinions. Considering the fact that people spend hours daily on social media and share their opinion on various different topics helps us analyze sentiments better. More and more companies are using social media tools to provide various services and interact with customers. Sentiment Analysis (SA) classifies the polarity of given tweets to positive and negative tweets in order to understand the sentiments of the public. This paper aims to perform sentiment analysis of real-time 2019 election twitter data using the feature selection model word2vec and the machine learning algorithm random forest for sentiment classification. Word2vec with Random Forest improves the accuracy of sentiment analysis significantly compared to traditional methods such as BOW and TF-IDF. Word2vec improves the quality of features by considering contextual semantics of words in a text hence improving the accuracy of machine learning and sentiment analysis.

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T2 - 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)

Y2 - 28 September 2019 through 29 September 2019

ER -

Real-Time Sentiment Analysis of 2019 Election Tweets using Word2vec and Random Forest Model

Abstract

Conference

Bibliographical note

Keywords

Access to Document

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Cite this