基于词典的朴素贝叶斯修正中文电影分类

S. Sunarti, I. Wahyono, H. Putranto, Djoko Saryono, Herri Akhmad Bukhori, Tiksno Widyatmoko, M. Rosli, Nurbiha A. Shukor, Noor Dayana Abdul Halim
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引用次数: 1

摘要

网上有很多学习汉语的中国电影,YouTube就是其中之一。这部教育片提供了消极和积极的评价。为了得到一个好的电影来学习汉语,我们需要对汉语学习的正面和负面评论评分进行分类,教师可以在这个视频中使用。此外,影评是中国电影分级的一种演变。对评论的评价包括故事叙述、内容、模型、视觉效果等。这篇评论有批评和评论,其中包括对这部电影对汉语学习的感受。评论员帮助电影学生将电影中的情绪与积极或消极的情绪群体进行比较。本研究将朴素贝叶斯分类法与基于词典的函数应用于评论的情感分析。分类过程考虑得分中情绪内容词的出现以及积极或消极情绪类的可能得分值。从测试结果来看,停止词排除形式的特征选择正确率、精密度和召回率分别为0.91、0.87和0.98。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lexicon-Based Features on Naive Bayes Modification for Classification of Chinese Film
There are many Chinese movies on the internet for learning Chinese, one of which is on YouTube. This educational film provides negative and positive comments. To get a good movie to learn Chinese, we need to classify positive and negative comments ratings for Chinese learning that teachers can use in this video. In addition, a review of comments is an evolution of Chinese film ratings. The evaluation of comments included includes storytelling, content, model, visual effects, and more. The review has criticisms and comments that include feelings about the movie on Chinese language learning. Commentator helps movie students compare a movie's mood with positive or negative emotion groups. This research uses the naive Bayes taxonomy with the Lexicon Based function in sentiment analysis of comments. The classification process considers the appearance of words of emotional content in the score and the possible score values for positive or negative emotional classes. Based on test results, feature selection accuracy, precision, and recall in the form of stop word exclusion receive scores of 0.91, 0.87, and 0.98, respectively.
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