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There are now many more people sharing their views, ideas, and opinions because of the proliferation of user-generated material on the Internet and the development of information technology. People frequently use social media platforms like Facebook, Instagram, WhatsApp, and Twitter to express their thoughts and feelings. Opinion analysis, which is the act of identifying emotional undertones from this enormous volume of Internet data, has become more important as a way of understanding public opinion, trends, and brand impressions. However, because of the distinctive features of the network, such as informal language, character constraints, and the quick influx of data, conducting sentiment analysis on Twitter data poses unique obstacles. By considering the above difficulties, we have proposed a machine learning-based approach to classify the emotions as either positive, negative, or neutral. This paper conducts a thorough analysis of sentiment on Twitter data using the Support Vector Machine (SVM), Maximum Entropy (Max Ent), and Naive Bayes Multinomial (NBM) machine learning algorithms. To do this experiment, we deal with the large volume of data collected from Kaggle. To train the machine learning model, this wide range of tweet datasets from different subjects and domains is preprocessed using the Natural Language Tool Kit (NLTK). The results from these models were tested using various testing matrices like precision, recall, and F1-score. We achieved a maximum accuracy of 97% for SVM compared to all three classifiers.

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Sentiment Analysis in Twitter Data Using Machine Learning-Based Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Kazi Abdullah Al Arafat,
  • Mahmudur Rahman Roni,
  • Sumaya Siddique,
  • Mohammad Abu Yousuf,
  • Mohammad Ali Moni

摘要

There are now many more people sharing their views, ideas, and opinions because of the proliferation of user-generated material on the Internet and the development of information technology. People frequently use social media platforms like Facebook, Instagram, WhatsApp, and Twitter to express their thoughts and feelings. Opinion analysis, which is the act of identifying emotional undertones from this enormous volume of Internet data, has become more important as a way of understanding public opinion, trends, and brand impressions. However, because of the distinctive features of the network, such as informal language, character constraints, and the quick influx of data, conducting sentiment analysis on Twitter data poses unique obstacles. By considering the above difficulties, we have proposed a machine learning-based approach to classify the emotions as either positive, negative, or neutral. This paper conducts a thorough analysis of sentiment on Twitter data using the Support Vector Machine (SVM), Maximum Entropy (Max Ent), and Naive Bayes Multinomial (NBM) machine learning algorithms. To do this experiment, we deal with the large volume of data collected from Kaggle. To train the machine learning model, this wide range of tweet datasets from different subjects and domains is preprocessed using the Natural Language Tool Kit (NLTK). The results from these models were tested using various testing matrices like precision, recall, and F1-score. We achieved a maximum accuracy of 97% for SVM compared to all three classifiers.