Tweets Classification using Different Classifiers for Sentiment Analysis

Authors

  • Sagar Pahariya Student, Department of Electronics, Madhav Institute of Technology & Science, Gwalior, India

Keywords:

Sentiment Analysis, Tweet classification, classification algorithm, Feature Exaction, XGB Alogorithm.

Abstract

Twitter is a famous platform for social networking where people share and engage with "tweets" comments. It is a way of expressing people's opinions or emotions on various topics. Different parties, like producers and consumers, have carried out a sentiment analysis on tweets to gain insights into goods or to analyze the sector. Also, the accuracy of our sentiment analysis forecasts will increase with the recent advances in machine learning algorithms. In this research, we will try to analyze sentiments in tweets using different techniques of ML. We try to determine if it's positive or negative with the polarity of the tweet. The more powerful sentiment should be chosen as the final tweet if both positive and negative elements are included. The Kaggle dataset was crawled and positively/negatively labeled. The presented data include emoticons, identification, date, query, usernames, and information that have to be processed into a standard form. It is also essential to extract useful features from the text that is a type of "tweet" representation. We have often developed numerous classifications to classify sentiments using the extracted features. The objective of this work is to evaluate the results of various machine learning algorithms in the measurement of Twitter data. The efficiency by sentence classification of Decision Tree, Random Forest & XGB algorithms was compared. The findings show that by using Decision Tree & XGB, Random Forest has achieved great accuracy.

 

References

F. A. Pozzi, E. Fersini, E. Messina and B. Liu, in Sentiment Analysis In Social Network, United States, Todd Green, 2017, p. 228.

S. Widoatmodjo, in Cara Cepat Memulai Investasi Saham Panduan Bagi Pemula, Jakarta, Kompas Media, 2012, p. 139.

Rajput, D.S., Thakur, R.S. and Basha, S.M. eds., 2018. Sentiment Analysis and Knowledge Discovery in Contemporary Business. IGI Global..

Mirjalili, S., Faris, H. and Aljarah, I., 2019. Evolutionary machine learning techniques. Springer.

Peng, Y.F. and Chou, T.R., 2019, April. Automatic color palette design using color image and sentiment analysis. In 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 389-392). IEEE.

Wongkar, M. and Angdresey, A., 2019, October. Sentiment analysis using Naive Bayes Algorithm of the data crawler: Twitter. In 2019 Fourth International Conference on Informatics and Computing (ICIC) (pp. 1-5). IEEE.

Xu, G., Yu, Z., Yao, H., Li, F., Meng, Y. and Wu, X., 2019. Chinese text sentiment analysis based on extended sentiment dictionary. IEEE Access, 7, pp.43749-43762.

Cheng, L.-C., & Tsai, S.-L. (2019). Deep learning for automated sentiment analysis of social media. Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. doi:10.1145/3341161.3344821

Lee, J. S., Zuba, D., & Pang, Y. (2019). Sentiment Analysis of Chinese Product Reviews using Gated Recurrent Unit. 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService). doi:10.1109/bigdataservice.2019.00030

Mandloi, L., & Patel, R. (2020). Twitter Sentiments Analysis Using Machine Learning Methods. 2020 International Conference for Emerging Technology (INCET). doi:10.1109/incet49848.2020.9154183.

AlSalman, H. (2020). An Improved Approach for Sentiment Analysis of Arabic Tweets in Twitter Social Media. 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS). doi:10.1109/iccais48893.2020.9096850

Downloads

Published

2022-01-15

Issue

Vol. 10 No. 3 (2021)

Section

Research Article

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.