【摘要】 针对微博网络舆情信息量大、无规则、随机变化的特点,提出TFIDF-NB(Term Frequency Inverse Document Frequency-Naive Bayes)用于微博情感分析,设计与实现了一个基于Scrapy框架的微博评论爬虫,将某热点事件的若干条微博评论进行爬取并存进数据库,然后进行文本分割、LDA (Latent Dirichlet Allocation)主题聚类,最后使用TFIDF-NB算法进行情感分类。实验结果表明,TFIDF-NB算法平均准确率高于线性支持向量机算法和K近邻算法,在精确率和召回率方面高于K近邻算法,具有较好的情感分类效果。更多还原

【Abstract】 In view of the large amount of public opinion information on Weibo, irregular and random changes, this paper proposes a Weibo sentiment analysis method based on TFIDF-NB( Term Frequency Inverse Document Frequency-Naive Bayes) algorithm. By coding a Weibo comment crawler based on the Scrapy framework, several Weibo comments on a hot event are crawled and stored in the database. Then text segmentation and LDA( Latent Dirichlet Allocation) topic clustering are performed. And finally the TFIDF-NB algorithm is used for sentiment classification. Experimental results show that the accuracy of the algorithm is higher than that of the standard linear Support Vector Machine algorithm and the K-Nearest Neighbor algorithm, and it is higher than the K-Nearest Neighbor algorithm in terms of accuracy and recall, and it has a better effect on sentiment classification.更多还原