Twitter Sentiment Analysis Using Machine Learning Techniques
DOI:
https://doi.org/10.32628/IJSRST251241Keywords:
sentiment analysis, Twitter, machine learning, natural language processing, text classification, social media analyticsAbstract
This paper presents an effective sentiment analysis system designed to classify the polarity of tweets into positive, negative, or neutral sentiments. The framework utilizes supervised machine learning algorithms, including Logistic Regression, Support Vector Machines (SVM), and Random Forest, trained on the Sentiment140 dataset. Text preprocessing techniques such as tokenization, stopword removal, stemming, and TF-IDF vectorization are applied to improve classification performance. The proposed system achieves an accuracy of 87.2% with SVM, outperforming other baseline models. This solution offers scalable deployment in social media monitoring, political campaign tracking, and customer feedback analysis.
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Y. Zhang, D. Jin, X. Xie, and H. Liu, “BERT-Based Sentiment Analysis for Social Media Texts,” Information Processing & Management, vol. 59, no. 2, pp. 102748, 2022. [DOI: 10.1016/j.ipm.2021.102748]
H. Liu, R. Wu, and K. Zhao, “Sentiment Analysis on COVID-19 Tweets Using RoBERTa,” IEEE Access, vol. 9, pp. 148566–148576, 2021. [DOI: 10.1109/ACCESS.2021.3124540]
A. Giachanou and F. Crestani, “Deep Learning for Sentiment Analysis: A Survey,” ACM Computing Surveys, vol. 54, no. 7, pp. 1–38, 2021. [DOI: 10.1145/3453152]
P. Nakov et al., “SemEval-2016 Task 4: Sentiment Analysis in Twitter,” Proceedings of SemEval-2016, pp. 1–18. [ACL Anthology: S16-1001]
A. Go, R. Bhayani, and L. Huang, “Twitter Sentiment Classification using Distant Supervision,” Stanford University, CS224N Project Report, 2009. (Foundational Reference) [Available at: http://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf]
T. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Proceedings of NAACL-HLT, pp. 4171–4186, 2019. [arXiv:1810.04805]
H. Singh and M. Sharma, “Aspect-Based Sentiment Analysis on Twitter Data Using BERT Embeddings,” International Journal of Information Management Data Insights, vol. 4, no. 1, 2024. [DOI: 10.1016/j.jjimei.2024.100179]
S. Wang and Z. Li, “Sentiment Analysis Using Pre-trained Language Models: A Case Study on Twitter,” IEEE Big Data, pp. 334–341, 2021. [IEEE Xplore]
A. Sun, C. Peng, and X. Li, “Detecting Sarcasm in Twitter with Contextualized Language Models,” Expert Systems with Applications, vol. 209, 2023. [DOI: 10.1016/j.eswa.2022.118258]
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Copyright (c) 2025 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
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