基于注意力的空间词嵌入Bi-LSTM情感分析模型

IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES
Kun Zhu, Nur Hana Samsudin
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引用次数: 0

摘要

电影评论为影迷群体提供了一种交流的媒介。影评不仅帮助观众和潜在观众获得对电影的总体看法,而且使影迷能够构建对电影的看法。在这项工作中,我们对6万多篇电影评论进行了分析,通过文本嵌入找到有意义的文本表示。以6万多篇影评文本数据作为训练集,2万多篇影评文本数据作为测试集,提出了基于注意力的双向长短期记忆(Bi-LSTM)网络,对文本嵌入进行了改进。基于数据特征,对单词和短语相关信息进行概率分析,将分析结果与文本嵌入相结合,对文本嵌入进行空间化处理,并将本文提出的基于注意力的空间化词嵌入Bi-LSTM模型与几种传统机器学习模型的性能进行比较。本文提出的基于注意力的空间化词嵌入Bi-LSTM模型在电影评论情感分类数据集上的F1得分为0.91,预测准确率为91%,优于目前的研究结果。该模型可以有效地识别电影评论的情感倾向,并利用分析的情感倾向来指导消费者的消费,获得对电影内容的反馈。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Attention-based Spatialized Word Embedding Bi-LSTM Model for Sentiment Analysis
Movie reviews provide a medium of communication for the movie fans community. Movie reviews not only help viewers and potential viewers to obtain a general opinion about a movie but also allow the fans to construct an opinion of the movie. In this work, an analysis of over 60,000 movie reviews has been implemented to find meaningful text representation via text embedding. We improved the text embedding by proposing an attention-based Bidirectional Long-Short Term Memory (Bi-LSTM) network by using over 60,000 movie review text data as the training set and over 20,000 movie review text data as the testing set. Based on the data features, we performed a probabilistic analysis of the information related to words and phrases, combined the analysis results with text embedding, spatialized the text embedding, and compared the performance of the proposed attention-based spatialized word embedding Bi-LSTM model with several traditional machine learning models. The attention-based spatialized word embedding Bi-LSTM model proposed in this paper achieves an F1 score of 0.91 on the movie review sentiment classification dataset, with a prediction accuracy of 91%, outperforming the results of the current state-of-the-art research. The model can effectively identify the sentimental tendencies of movie reviews and use the analyzed sentimental tendencies to guide consumers in their consumption and obtain feedback on movie content.
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来源期刊
Pertanika Journal of Science and Technology
Pertanika Journal of Science and Technology MULTIDISCIPLINARY SCIENCES-
CiteScore
1.50
自引率
16.70%
发文量
178
期刊介绍: Pertanika Journal of Science and Technology aims to provide a forum for high quality research related to science and engineering research. Areas relevant to the scope of the journal include: bioinformatics, bioscience, biotechnology and bio-molecular sciences, chemistry, computer science, ecology, engineering, engineering design, environmental control and management, mathematics and statistics, medicine and health sciences, nanotechnology, physics, safety and emergency management, and related fields of study.
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