Public Opinion Analysis of Weibo Comments Based on Crawler and SVM

Zhang, Haohong; Li, Shaohua; Feng, Jingying; Liang, Yiduo

doi:10.1109/imcec51613.2021.9482219

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2022

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“…By building the word bag model, a total of 87742-word dictionaries and corresponding word bag text vectors are established. On this basis, TF-IDF model in the gensim library is called to calculate TF-IDF values of the document sparse matrix as the text feature vector [10].…”

Section: Methodsmentioning

confidence: 99%

Sentiment Analysis of Weibo Comments based on LDA Model

Chen¹

2022

HSET

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The essence of LDA (Latent Dirichlet Allocation) model is a generative Bayesian probability model that contains three layers of words, topics and corpus (sometimes called document set). Under the LDA algorithm theory, each document represents a probability distribution formed by some topics, and each topic represents a probability distribution formed by many words. Therefore, the model fitting results will present the core keywords and specific probabilities of each topic, and researchers can interpret the meaning of the document according to the model results. In this paper, we hope to use the LDA model as the basis for the emotional analysis of microblog comments.

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Section: Methodsmentioning

confidence: 99%

Sentiment Analysis of Weibo Comments based on LDA Model

Chen¹

2022

HSET

View full text Add to dashboard Cite

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Public Opinion Analysis of Weibo Comments Based on Crawler and SVM

Cited by 1 publication

References 2 publications

Sentiment Analysis of Weibo Comments based on LDA Model

Sentiment Analysis of Weibo Comments based on LDA Model

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