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<p>Social media serves as a platform to unite individuals with similar interests and perspectives. Twitter, in particular, stands out as a pioneer, significantly influencing people's daily lives by providing a space to express opinions. However, relying solely on hashtags for tweet recommendations might not be the most accurate approach. To enhance this, our focus has been on developing a recommendation system based on both topic and sentiment analysis. Our methodology involved aggregating diverse datasets from sources like Kaggle and merging them into a comprehensive dataset. Employing various models, including LSTM, Logistic Regression, and SVM, we aimed to create a system that enables groups to share their opinions effectively. In our evaluations, the LSTM model showcased remarkable accuracy at 83%, followed closely by Logistic Regression at 82%, and SVM at 81%. The superior performance of the LSTM model signifies its effectiveness in accurately identifying tweet sentiments and topics through deep learning techniques. In essence, our system successfully deciphers sentiments and topics within tweets, fostering a more nuanced and efficient means of communication among users.</p> Graphical Abstract <p></p>

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System and Method for Identifying the Sentiment and Topic of a User Tweet with a Deep Learning Technique

  • 期刊论文

  • 发表日期：

    2026年6月17日

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  • Chinta Someswara Rao,
  • Balaka Ramesh Naidu,
  • Katari Butchi Raju,
  • S Rama Gopala Reddy

摘要

Social media serves as a platform to unite individuals with similar interests and perspectives. Twitter, in particular, stands out as a pioneer, significantly influencing people's daily lives by providing a space to express opinions. However, relying solely on hashtags for tweet recommendations might not be the most accurate approach. To enhance this, our focus has been on developing a recommendation system based on both topic and sentiment analysis. Our methodology involved aggregating diverse datasets from sources like Kaggle and merging them into a comprehensive dataset. Employing various models, including LSTM, Logistic Regression, and SVM, we aimed to create a system that enables groups to share their opinions effectively. In our evaluations, the LSTM model showcased remarkable accuracy at 83%, followed closely by Logistic Regression at 82%, and SVM at 81%. The superior performance of the LSTM model signifies its effectiveness in accurately identifying tweet sentiments and topics through deep learning techniques. In essence, our system successfully deciphers sentiments and topics within tweets, fostering a more nuanced and efficient means of communication among users.

Graphical Abstract