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Sentiment Analysis of Movie Reviews Based on Sentiment Dictionary and Deep Learning Models

Published: 28 June 2024 Publication History

Abstract

As the Internet era has progressed, platforms such as Douban Movies have spawned a great number of evaluations with personal biases. However, these assessments lack a set length, and the text's expression is varied, not constrained to grammar-related constraints. The expressive style is less formal. As a result, mining and assessing these opinions has substantial economic worth. This experiment utilized a novel sentiment lexicon to adapt informal vocabulary in movie reviews. To improve the accuracy of sentiment analysis in movie reviews, it was integrated with the Albert-BiLSTM-Attention model. The results of six rounds of comparative experiments show that the method suggested in this paper has improved average precision, average recall, and average F1 score in the sentiment classification of this dataset. The suggested model can be used to achieve precise sentiment analysis for film reviews, offering pertinent support and advice for the production team's upcoming films.

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  1. Sentiment Analysis of Movie Reviews Based on Sentiment Dictionary and Deep Learning Models

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    ICRSA '23: Proceedings of the 2023 6th International Conference on Robot Systems and Applications
    September 2023
    335 pages
    ISBN:9798400708039
    DOI:10.1145/3655532
    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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    Published: 28 June 2024

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    Author Tags

    1. Albert-BiLSTM
    2. Attention mechanisms
    3. deep learning
    4. sentiment lexicon

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