【摘要】 电影评论文本的情感分析是针对文本数据中隐藏的情感信息进行提取和情感分类,从而帮助媒体平台等网络人员进行观众对电影喜好程度分析。基于此,本文提出一种基于情感词典与机器学习中SVM分类技术相结合的电影评论文本情感分析方法。首先,构造基础情感词典、领域情感词典、否定词词典和程度副词词典,四部词典相结合实现了对词典的扩充;其次,通过计算情感权值与用户评分相结合的方式构造SVM模型训练集;最后,运用测试数据进行情感分类实验,结果表明该方法相比于基于一部基础情感词典的方法具有更高的情感分类正确率。更多还原

【Abstract】 The sentiment analysis of the film review text is to extract and analyze the hidden sentiment information in the text data, thereby helping the network personnel such as the media platform to analyze the audience’s preference for the film.Based on this, this paper proposes a film commentary text sentiment analysis method based on SVM classification technology in sentiment dictionary and machine learning. Firstly, the basic sentiment dictionary, the domain sentiment dictionary, the negative word dictionary and the degree adverb dictionary are constructed. The four dictionaries are combined to realize the expansion of the dictionary. Secondly, the SVM model training set is constructed by calculating the combination of sentiment weight and user scoring. Finally, using the test data for sentiment classification experiments, the results show that the method has higher accuracy of sentiment classification than the method based on a basic sentiment dictionary.更多还原