## 1 Introduction
### [PERSON]  
Also known as the Latent Semantic Index, Latent Semantic Analysis (LSA) preserves the method of calculating the similarity between terms and documents expressed by space vectors by using the angle between the space vectors in the traditional vector space model. Besides, the words and documents are further mapped into the latent semantic space, along with the mining of the underlying semantics and latent topics under the text and document surface, thereby improving the effect of information retrieval. The essence of the latent semantic analysis method is to find out the true semantics of the vocabulary in the document, and then dig out the topic not dependent on vocabulary in the document, that is, the latent semantic theme, to address the related problems and deficiencies caused by the inability to consider the latent semantic in the Vector Space Model [14]. Because LSA expresses each word as a point in the latent semantic space, multiple meanings of a word correspond to a point in this space, which are not distinguished, helping explain that it only solves the problem of \"Synonymy\", but not \"Polysemy\". Probabilistic Latent Semantic Analysis (PLSA) was firstly proposed by [PERSON] abroad based on statistical latent block model, applying it for unsupervised learning. Experimentalresults show that the method, when compared with standard latent semantic analysis, marks an obvious progress. Later, in another article, [PERSON] introduced the probabilistic latent semantic analysis method and applied it to automatic retrieval. The experimental results show that the probabilistic latent semantic retrieval significantly improves accuracy compared to the matching of standard terms and latent semantic analysis. Derived from the statistical perspective of latent semantic analysis, the probabilistic latent semantic analysis method is a new statistical technique for analyzing two-mode and co-occurrence data. More specifically, it has been widely used in many related fields such as information extraction and information filtering, natural language processing, and machine learning [15,16]. In terms of the most general application, it means that standard statistical techniques can be applied to text filtering, text selection and complexity control [17]. For example, marginal trust can be used to estimate the performance of the PLSA by calculating the classification results of the PLSA. More specifically, the PLSA associates latent semantic variables with each co-occurring word are all polar [18]. Based on mathematical statistics, traditional text features extraction methods ignore the semantic relationship between words in the text, as well as the semantic problems caused by \"Synonymy\" and \"Polysemy\" [19]. Probabilistic latent semantic analysis is a probabilistic latent layer semantic analysis method based on statistical latent block model, which can obviously improve accuracy compared with standard items and latent semantic analysis. Besides, derived from the statistical viewpoint of latent semantic analysis, probabilistic latent semantic analysis method can make the latent semantic information with probabilistic features as the classification feature, thus effectively reducing the negative effects exerted by \"Synonymy\" and \"Polysemy\" in natural language, which in turn improves the emotional classification effect. In the next section, we will introduce the Fisher kernel function and the reason for choosing it. More specifically, the Fisher kernel function can be used to measure the similarity between two objects on the generated model set and the statistical model set. Combined with the probabilistic latent semantic analysis method, it constructs a new kernel function by considering the probabilistic features of the latent semantics in the inner product of the kernel function. As one of the similar methods for comparison [6], PLSA-SVM does not make any improvement to the kernel function, only transferring to the support vector machine for classification using the topic features in PLSA.