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Sentiment Analysis of Novel Review Using Long Short-Term Memory Method

机译：长篇短记忆法的小说评论情感分析

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摘要

The rapid development of the internet and social media and a large amount of text data has become an important research subject in obtaining information from the text data. In recent years, there has been an increase in research on sentiment analysis in the review text to determine the polarity of opinion on social media. However, there are still few studies that apply the deep learning method, namely Long Short-Term Memory for sentiment analysis in Indonesian texts. This study aims to classify Indonesian novel novels based on positive, neutral and negative sentiments using the Long Short-Term Memory (LSTM) method. The dataset used is a review of Indonesian language novels taken from the goodreads.com site. In the testing process, the LSTM method will be compared with the Na?ve Bayes method based on the calculation of the values of accuracy, precision, recall, f-measure. Based on the test results show that the Long Short-Term Memory method has better accuracy results than the Na?ve Bayes method with an accuracy value of 72.85%, 73% precision, 72% recall, and 72% f-measure compared to the results of the Na?ve Bayes method accuracy with accuracy value of 67.88%, precision 69%, recall 68%, and f-measure 68%.
机译：互联网和社交媒体的快速发展以及大量的文本数据已经成为从文本数据中获取信息的重要研究课题。近年来，评论文本中用于确定社交媒体观点极性的情感分析研究有所增加。但是，仍然很少有应用深度学习方法的研究，即用于印尼文本中情感分析的长短期记忆。这项研究旨在使用长短期记忆（LSTM）方法根据正面，中性和负面情绪对印尼小说进行分类。使用的数据集是对来自goodreads.com网站的印尼语小说的评论。在测试过程中，将基于准确性，精确度，召回率和f量度值的计算，将LSTM方法与朴素贝叶斯方法进行比较。根据测试结果，与Naveve Bayes方法相比，Long Short-Term Memory方法具有更好的准确性结果，与Na-ve Bayes方法相比，其准确性值为72.85％，73％精度，72％召回率和72％f-measure。朴素贝叶斯方法准确度的结果，准确度值为67.88％，精确度为69％，召回率为68％和f测度为68％。

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