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March 14, 20260 citationsOpen Access

Twitter Sentiment Analysis Using Deep Learning Algorithms

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BB.M.S.NikithBLB Lakshmi

Key Points

  • The central aim is to develop a deep learning model for classifying Twitter sentiments into categories.
  • Developed a deep learning model using neural network architectures.
  • Analyzed Twitter data to classify sentiments as positive, negative, or neutral.
  • Leveraged semantic and syntactic patterns in tweets for effective classification.
  • Achieved accurate sentiment detection across the three categories.
  • Provided valuable insights for applications in social media monitoring and opinion analysis.

Abstract

This study presents a deep learning approach for sentiment analysis on Twitter data. The proposed model classifies tweets into three categories: positive, negative, and neutral. By leveraging neural network architectures, the method effectively captures the semantic and syntactic patterns of social media text. The results demonstrate accurate sentiment detection, providing insights for social media monitoring, opinion analysis, and market research applications.

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Cite This Study

B.M.S.Nikith et al. (2026) studied this question.

synapsesocial.com/papers/69b4fc33b39f7826a300ceb3https://doi.org/10.5281/zenodo.18979247
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