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Research on Text Emotion Analysis Based on LSTM

Published: 15 December 2023 Publication History

Abstract

Sentiment analysis of text based on deep learning has become an active research direction in the field of natural language processing, where the sentiment analysis of text is used to automatically identify the sentiment tendency embedded in the text, such as positive or negative. Sentiment analysis based on deep learning usually adopts neural network models, which are trained on a large amount of labelled data to capture the sentiment information in the text. The commonly used neural network models include Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and so on. In this study, Long Short-Term Memory (LSTM) is used to build a model to construct a text sentiment analyzer, which, after testing 23982 textual data, can predict whether the sentiment expressed is negative or positive according to the input statements, and the accuracy of its prediction results can reach 90.37%. The experimental results show that it is more flexible and generalizable than the traditional rule-based or feature engineering methods, and can further improve the performance of sentiment analysis.

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          ICCVIT '23: Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology
          August 2023
          378 pages
          ISBN:9798400708701
          DOI:10.1145/3627341
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 15 December 2023

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          Author Tags

          1. LSTM
          2. Neural Network Model
          3. Sentiment Classification
          4. Text Sentiment Analysis

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          ICCVIT 2023

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          ICCVIT '23 Paper Acceptance Rate 54 of 142 submissions, 38%;
          Overall Acceptance Rate 54 of 142 submissions, 38%

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