Abstract
At present, in the mainstream sentiment analysis methods represented by the Support Vector Machine, the vocabulary and the latent semantic information involved in the text are not well considered, and sentiment analysis of text is dependent overly on the statistics of sentiment words. Thus, a Fisher kernel function based on Probabilistic Latent Semantic Analysis is proposed in this paper for sentiment analysis by Support Vector Machine. The Fisher kernel function based on the model is derived from the Probabilistic Latent Semantic Analysis model. By means of this method, latent semantic information involving the probability characteristics can be used as the classification characteristics, along with the improvement of the effect of classification for support vector machine, and the problem of ignoring the latent semantic characteristics in text sentiment analysis can be addressed. The results show that the effect of the method proposed in this paper, compared with the comparison method, is obviously improved.
Highlights
As an emerging field, text sentiment analysis has great potential in research and practical applications, helping explain that the research of text sentiment analysis has been attracting increasingly more attention at home and abroad [1,2,3]
The histogram of words appearing in the document is mainly used as the feature of the document in HIST-SVM method, and the histogram feature is submitted to the SVM for text sentiment analysis
A Fisher kernel function method based on probabilistic latent semantic analysis is proposed in this paper, which improves the kernel function of support vector machine
Summary
Text sentiment analysis has great potential in research and practical applications, helping explain that the research of text sentiment analysis has been attracting increasingly more attention at home and abroad [1,2,3]. Statistical methods, such as keyword matching method, are adopted in most of the thematic information mining for information mining. Other features, such as Semantic Structure, the latent semantic information of documents, are ignored [6] Overall, these problems can influence the degree of coincidence between the sentiment analysis and the actual semantics, affecting the accuracy of the sentiment analysis. How to solve these problems and improve the accuracy of sentiment analysis has become a challenging research topic
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