Classifying political tweets using naïve bayes and support vector machines

Ahmed Al Hamoud; Ali Alwehaibi; Kaushik Roy; Marwan Bikdash

doi:10.1007/978-3-319-92058-0_71

Classifying political tweets using naïve bayes and support vector machines

Ahmed Al Hamoud, Ali Alwehaibi, Kaushik Roy, Marwan Bikdash

Industrial and systems engineering with North Carolina A&T State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Twitter, which is one of the most popular microblogging platforms and contains a huge amount of meaningful information, can be used in opinion mining and sentiment analysis. Twitter data contains text communication of more than 330 million active users monthly. This research effort applies the machine learning techniques to determine whether the contents of tweets are political or apolitical. Preprocessing involves cleaning-up the texts to obtain meaningful information and accurate opinions. Bag-of-Words (BOW), Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) were used to extract the features from twitter data. We then used Chi-Square technique to select the salient features from a high dimensional feature set. Finally, Support Vector Machines (SVMs) and Naive Bayes (NB) were applied to classify the twitter data. The results suggest that SVMs with BOW provide the highest accuracy and F-measure.

Original language	English
Title of host publication	Recent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings
Editors	Otmane Ait Mohamed, Malek Mouhoub, Samira Sadaoui, Moonis Ali
Publisher	Springer Verlag
Pages	736-744
Number of pages	9
ISBN (Print)	9783319920573
DOIs	https://doi.org/10.1007/978-3-319-92058-0_71
State	Published - 2018
Externally published	Yes
Event	31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA/AIE 2018 - Montreal, Canada Duration: Jun 25 2018 → Jun 28 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10868 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA/AIE 2018
Country/Territory	Canada
City	Montreal
Period	06/25/18 → 06/28/18

Keywords

Feature selection
Natural language processing
Opinion mining
Sentiment analysis

Access to Document

10.1007/978-3-319-92058-0_71

Cite this

Al Hamoud, A., Alwehaibi, A., Roy, K., & Bikdash, M. (2018). Classifying political tweets using naïve bayes and support vector machines. In O. Ait Mohamed, M. Mouhoub, S. Sadaoui, & M. Ali (Eds.), Recent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings (pp. 736-744). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10868 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-92058-0_71

Al Hamoud, Ahmed ; Alwehaibi, Ali ; Roy, Kaushik et al. / Classifying political tweets using naïve bayes and support vector machines. Recent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings. editor / Otmane Ait Mohamed ; Malek Mouhoub ; Samira Sadaoui ; Moonis Ali. Springer Verlag, 2018. pp. 736-744 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{496e174c31d947139a6db7d8ef39d670,

title = "Classifying political tweets using na{\"i}ve bayes and support vector machines",

abstract = "Twitter, which is one of the most popular microblogging platforms and contains a huge amount of meaningful information, can be used in opinion mining and sentiment analysis. Twitter data contains text communication of more than 330 million active users monthly. This research effort applies the machine learning techniques to determine whether the contents of tweets are political or apolitical. Preprocessing involves cleaning-up the texts to obtain meaningful information and accurate opinions. Bag-of-Words (BOW), Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) were used to extract the features from twitter data. We then used Chi-Square technique to select the salient features from a high dimensional feature set. Finally, Support Vector Machines (SVMs) and Naive Bayes (NB) were applied to classify the twitter data. The results suggest that SVMs with BOW provide the highest accuracy and F-measure.",

keywords = "Feature selection, Natural language processing, Opinion mining, Sentiment analysis",

author = "\{Al Hamoud\}, Ahmed and Ali Alwehaibi and Kaushik Roy and Marwan Bikdash",

note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA/AIE 2018 ; Conference date: 25-06-2018 Through 28-06-2018",

year = "2018",

doi = "10.1007/978-3-319-92058-0\_71",

language = "English",

isbn = "9783319920573",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "736--744",

editor = "\{Ait Mohamed\}, Otmane and Malek Mouhoub and Samira Sadaoui and Moonis Ali",

booktitle = "Recent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings",

}

Al Hamoud, A, Alwehaibi, A, Roy, K & Bikdash, M 2018, Classifying political tweets using naïve bayes and support vector machines. in O Ait Mohamed, M Mouhoub, S Sadaoui & M Ali (eds), Recent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10868 LNAI, Springer Verlag, pp. 736-744, 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA/AIE 2018, Montreal, Canada, 06/25/18. https://doi.org/10.1007/978-3-319-92058-0_71

Classifying political tweets using naïve bayes and support vector machines. / Al Hamoud, Ahmed; Alwehaibi, Ali; Roy, Kaushik et al.
Recent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings. ed. / Otmane Ait Mohamed; Malek Mouhoub; Samira Sadaoui; Moonis Ali. Springer Verlag, 2018. p. 736-744 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10868 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classifying political tweets using naïve bayes and support vector machines

AU - Al Hamoud, Ahmed

AU - Alwehaibi, Ali

AU - Roy, Kaushik

AU - Bikdash, Marwan

PY - 2018

Y1 - 2018

N2 - Twitter, which is one of the most popular microblogging platforms and contains a huge amount of meaningful information, can be used in opinion mining and sentiment analysis. Twitter data contains text communication of more than 330 million active users monthly. This research effort applies the machine learning techniques to determine whether the contents of tweets are political or apolitical. Preprocessing involves cleaning-up the texts to obtain meaningful information and accurate opinions. Bag-of-Words (BOW), Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) were used to extract the features from twitter data. We then used Chi-Square technique to select the salient features from a high dimensional feature set. Finally, Support Vector Machines (SVMs) and Naive Bayes (NB) were applied to classify the twitter data. The results suggest that SVMs with BOW provide the highest accuracy and F-measure.

AB - Twitter, which is one of the most popular microblogging platforms and contains a huge amount of meaningful information, can be used in opinion mining and sentiment analysis. Twitter data contains text communication of more than 330 million active users monthly. This research effort applies the machine learning techniques to determine whether the contents of tweets are political or apolitical. Preprocessing involves cleaning-up the texts to obtain meaningful information and accurate opinions. Bag-of-Words (BOW), Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) were used to extract the features from twitter data. We then used Chi-Square technique to select the salient features from a high dimensional feature set. Finally, Support Vector Machines (SVMs) and Naive Bayes (NB) were applied to classify the twitter data. The results suggest that SVMs with BOW provide the highest accuracy and F-measure.

KW - Feature selection

KW - Natural language processing

KW - Opinion mining

KW - Sentiment analysis

UR - https://www.scopus.com/pages/publications/85049041052

U2 - 10.1007/978-3-319-92058-0_71

DO - 10.1007/978-3-319-92058-0_71

M3 - Conference contribution

SN - 9783319920573

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 736

EP - 744

BT - Recent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings

A2 - Ait Mohamed, Otmane

A2 - Mouhoub, Malek

A2 - Sadaoui, Samira

A2 - Ali, Moonis

PB - Springer Verlag

T2 - 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA/AIE 2018

Y2 - 25 June 2018 through 28 June 2018

ER -

Al Hamoud A, Alwehaibi A, Roy K , Bikdash M. Classifying political tweets using naïve bayes and support vector machines. In Ait Mohamed O, Mouhoub M, Sadaoui S, Ali M, editors, Recent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings. Springer Verlag. 2018. p. 736-744. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-92058-0_71

Classifying political tweets using naïve bayes and support vector machines

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Keywords

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