Research Article
A Survey on Twitter Sentiment Analysis Using Machine Learning Techniques
@INPROCEEDINGS{10.1007/978-3-031-66044-3_22, author={G. Srikanth and K. Gangadhara Rao and Ramu Kuchipudi and Palamakula Ramesh Babu and R. Sai Venkat and T. Satyanarayana Murthy and G. Venakata Kishore}, title={A Survey on Twitter Sentiment Analysis Using Machine Learning Techniques}, proceedings={Pervasive Knowledge and Collective Intelligence on Web and Social Media. Second EAI International Conference, PerSOM 2023, Hyderabad, India, November 24--25, 2023, Proceedings}, proceedings_a={PERSOM}, year={2024}, month={8}, keywords={twitter sentiment analysis BernoulliNB LinearSVC Logistic Regression}, doi={10.1007/978-3-031-66044-3_22} }- G. Srikanth
K. Gangadhara Rao
Ramu Kuchipudi
Palamakula Ramesh Babu
R. Sai Venkat
T. Satyanarayana Murthy
G. Venakata Kishore
Year: 2024
A Survey on Twitter Sentiment Analysis Using Machine Learning Techniques
PERSOM
Springer
DOI: 10.1007/978-3-031-66044-3_22
Abstract
Twitter sentiment analysis involves various stages of the analysis process includes Text preprocessing techniques are applied to prepare the data, followed by examining the distribution of sentiment analysis. By utilizing the TF-IDF vectorizer, the textual data is transformed into numerical feature vectors. Three machine learning models, namely Bernoulli Naive Bayes (BernoulliNB), Linear Support Vector Classification (LinearSVC), and Logistic Regression, are created and evaluated using standard performance metrics like accuracy, precision, recall, and F1 score. The evaluation results effectively showcase the performance of each sentiment analysis model. The data is sourced from Twitter. The Logistic Regression model stands out in accurately classifying sentiments, while the LinearSVC and BernoulliNB models also exhibit high performance. The trained models are saved for future utilization, facilitating their integration into practical applications. This study presents a comprehensive approach to Twitter sentiment analysis, encompassing data preprocessing, model development, model evaluation, and storage for Twitter sentiment analysis tasks.
