中圖分類號： TN011；TP391.41
文獻標識碼： A
DOI：10.16157/j.issn.0258-7998.200748
中文引用格式： 楊戈，楊麓濤. 基于爬蟲和TFIDF-NB算法的微博情感分析[J].電子技術應用，2021，47(4)：59-62，66.
英文引用格式： Yang Ge，Yang Lutao. Sentiment analysis of Weibo based on TFIDF-NB algorithm[J]. Application of Electronic Technique，2021，47(4)：59-62，66.

Sentiment analysis of Weibo based on TFIDF-NB algorithm

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.

Key words : Weibo public opinion；web crawler；sentiment classification