Sentiment analysis for social media using SVM classifier of machine learning

Research Article
Open access

Sentiment analysis for social media using SVM classifier of machine learning

Qingyu Huang 1*
  • 1 College of Liberal Arts & Sciences, University of Illinois at Urbana-Champaign, Champaign, Illinois, 61820, United States    
  • *corresponding author qingyuh2@illinois.edu
ACE Vol.4
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-55-3
ISBN (Online): 978-1-915371-56-0

Abstract

The community’s perspectives and comments are a valuable resource for businesses and other organizations. In the past, businesses used inefficient procedures. Now that social media is the new trend, it enables an unprecedented level of analysis and evaluation. This enables unprecedented analysis and evaluation of various factors. This enables unprecedented analysis and evaluation of a wide range of topics and components in different contexts and settings. Throughout business history, these strategies have been expected. This field of study is called “sentiment analysis.” SVM was used to analyze sentiment for this research project. One of these duties required an SVM(SVM). Support vector machines, or SVM, is a popular supervised machine learning algorithm for determining text polarity. SVM abbreviates support vector machines. Precision, recall, and F-measure are used to evaluate SVM using two datasets of pre-classified tweets. Tables and graphs are used to communicate research findings. This research classifies tweets about US-Airlines and performs sentiment analysis with an accuracy of 91.8 percent, precision of 91.3 percent, and recall of 82.3 percent, as well as the F1 of 86.9 percent.

Keywords:

SVM, Classifier, Sentiment Analysis, Emotions, US-Airlines

Huang,Q. (2023). Sentiment analysis for social media using SVM classifier of machine learning. Applied and Computational Engineering,4,86-90.
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References

[1]. Ahmad, M., & Aftab, S. (2017). Analyzing the Performance of SVM for Polarity Detection with Different Datasets. International Journal of Modern Education and Computer Science(IJMECS), 9(10), 29- 36.

[2]. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135.

[3]. Saif, H., He, Y., Fernandez, M., & Alani, H. (2016). Contextual semantics for sentiment analysis of Twitter. Information Processing & Management, 52(1), 5-19.

[4]. Ahmad, M., Aftab, S., Ali, I., & Hameed, N. (2017). Hybrid Tools and Techniques for Sentiment Analysis: A Review. Int. J. Multidiscip. Sci. Eng, 8(3).

[5]. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.

[6]. Ahmad, M., Aftab, S., Muhammad, S. S., & Waheed, U. (2017). Tools and Techniques for Lexicon Driven Sentiment Analysis: A Review. Int. J. Multidiscip. Sci. Eng, 8(1), 17-23.

[7]. Cortes, C., & Vapnik, V. (1995). Support vector machine. Machine learning, 20(3), 273-297

[8]. Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79-86). Association for Computational Linguistics.

[9]. Zgheib, W. A., & Barbar, A. M. (2017). A study using support vector machines to classify the sentiments of tweets. International Journal of Computer Applications, 975, 8887.

[10]. Arora, R. (2012). Comparative analysis of classification algorithms on different datasets using WEKA. International Journal of Computer Applications, 54(13).

[11]. Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N project report, Stanford, 1(12), 2009.

[12]. Shoukry, A., & Rafea, A. (2012, May). Sentence-level Arabic sentiment analysis. In 2012 international conference on collaboration technologies and systems (CTS) (pp. 546-550). IEEE.

[13]. Twitter US Airline Sentiment from https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment.

[14]. Altawaier, M. M., & Tiun, S. (2016). Comparison of Machine Learning Approaches on Arabic Twitter Sentiment Analysis. International Journal on Advanced Science, Engineering and Information Technology, 6(6), 1067-1073.

[15]. Isa, D., Lee, L. H., Kallimani, V. P., & Rajkumar, R. (2008). Text document preprocessing with the Bayes formula for classification using the support vector machine. IEEE Transactions on Knowledge and Data engineering, 20(9), 1264-1272.

Cite this article

Huang,Q. (2023). Sentiment analysis for social media using SVM classifier of machine learning. Applied and Computational Engineering,4,86-90.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-55-3(Print) / 978-1-915371-56-0(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.4
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Ahmad, M., & Aftab, S. (2017). Analyzing the Performance of SVM for Polarity Detection with Different Datasets. International Journal of Modern Education and Computer Science(IJMECS), 9(10), 29- 36.

[2]. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135.

[3]. Saif, H., He, Y., Fernandez, M., & Alani, H. (2016). Contextual semantics for sentiment analysis of Twitter. Information Processing & Management, 52(1), 5-19.

[4]. Ahmad, M., Aftab, S., Ali, I., & Hameed, N. (2017). Hybrid Tools and Techniques for Sentiment Analysis: A Review. Int. J. Multidiscip. Sci. Eng, 8(3).

[5]. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.

[6]. Ahmad, M., Aftab, S., Muhammad, S. S., & Waheed, U. (2017). Tools and Techniques for Lexicon Driven Sentiment Analysis: A Review. Int. J. Multidiscip. Sci. Eng, 8(1), 17-23.

[7]. Cortes, C., & Vapnik, V. (1995). Support vector machine. Machine learning, 20(3), 273-297

[8]. Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79-86). Association for Computational Linguistics.

[9]. Zgheib, W. A., & Barbar, A. M. (2017). A study using support vector machines to classify the sentiments of tweets. International Journal of Computer Applications, 975, 8887.

[10]. Arora, R. (2012). Comparative analysis of classification algorithms on different datasets using WEKA. International Journal of Computer Applications, 54(13).

[11]. Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N project report, Stanford, 1(12), 2009.

[12]. Shoukry, A., & Rafea, A. (2012, May). Sentence-level Arabic sentiment analysis. In 2012 international conference on collaboration technologies and systems (CTS) (pp. 546-550). IEEE.

[13]. Twitter US Airline Sentiment from https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment.

[14]. Altawaier, M. M., & Tiun, S. (2016). Comparison of Machine Learning Approaches on Arabic Twitter Sentiment Analysis. International Journal on Advanced Science, Engineering and Information Technology, 6(6), 1067-1073.

[15]. Isa, D., Lee, L. H., Kallimani, V. P., & Rajkumar, R. (2008). Text document preprocessing with the Bayes formula for classification using the support vector machine. IEEE Transactions on Knowledge and Data engineering, 20(9), 1264-1272.

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