基于LSTM方法的Trip Advisor旅游对象情感分析

Novita Hanafiah, Yanto Setiawan, Aldi Buntaran, Muhammad Reynaldi
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引用次数: 0

摘要

本研究开发了一个旅游网站评论的情感分析应用程序。它用于帮助想要了解旅游景点信息的人获得正面或负面信息。情感分析采用LSTM方法。LSTM体系结构的确定包括抓取数据、手工标注、预处理(折叠大小写、删除标点、删除停止词、标记化和词序化)、word2index、词嵌入和LSTM层。为了达到最佳精度,需要确定合适的嵌入方法、dropout层的总层数和LSTM。研究结果表明,使用LSTM方法进行情感分析的准确率和损失分别为96.71%和14.22%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentiment Analysis of Tourism Objects on Trip Advisor Using LSTM Method
This study developed a sentiment analysis application for comments on tourist sites. It is used to help people who want to know about tourist attractions information to get positive or negative information. The method used to analyze the sentiment was LSTM. The determination of LSTM architecture consists of scraping data, manual labelling, preprocessing (case folding, removing punctuation, removing stopwords, tokenization, and lemmatization), word2index, word embedding, and LSTM layer. In order to achieve optimal accuracy, it is necessary to determine the right embedded method, the total number of layers for the dropout layer, and LSTM. The performance of this study showed that the accuracy and loss from sentiment analysis using the LSTM method were 96.71% and 14.22%.
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