What does this research mean for the field?

A deep learning model effectively classifies Twitter sentiment into positive, negative, and neutral categories with high accuracy. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The central aim is to develop a deep learning model for classifying Twitter sentiments into categories.

March 14, 2026Open Access

Twitter Sentiment Analysis Using Deep Learning Algorithms

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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