基于词向量的影评情感分析方法探讨了影评的适用性

Fulian Yin, Yanyan Wang, Xingyi Pan, Pei Su
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引用次数: 1

摘要

基于词嵌入方法,提出了一种基于词向量的影评情感分析方法。结果表明,该方法的分类准确率达到了86.18%。同时,该方法适用于多种语言,如中文和英文,并可扩展到更大规模的内容。此外,本文还讨论了词向量维数对情感分析精度的影响以及该方法对不同长度句子的适用性。实验结果证明,基于词向量的情感评论分析方法不仅是一种高效、简单的情感表达分析方法,而且对不同长度、多语言的评论具有可扩展性和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Word Vector Based Review Vector Method for Sentiment Analysis of Movie Reviews Exploring the Applicability of the Movie Reviews
Based on word embedding method, this paper presents a word vector based review vector method for sentiment analysis of movie reviews. As a result, it is achieved that 86.18% classification accuracy using the method. Meanwhile, the method is applicable to multiple languages such as Chinese and English, and it is extensible for larger scale contents as well. What’s more, the influence of word vector dimensions on the sentiment analysis accuracy and the method’s applicability on sentences of varied lengths are also discussed in this paper. The experimental result proved that the word vector based review method for sentiment analysis is not only an efficient and simple way to analyze emotional expression, but also has extensibility and applicability for comments in varied lengths and multiple languages.
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