Abstract
The product online review text contains a large number of opinions and emotions. In order to identify the public’s emotional and tendentious information, we present reinforcement learning models in which sentiment classification algorithms of product online review corpus are discussed in this paper. In order to explore the classification effect of different sentiment classification algorithms, we conducted a research on Naive Bayesian algorithm, support vector machine algorithm, and neural network algorithm and carried out some comparison using a concrete example. The evaluation indexes and the three algorithms are compared in different lengths of sentence and word vector dimensions. The results present that neural network algorithm is effective in the sentiment classification of product online review corpus.
Highlights
In the field of natural language processing, emotion analysis has always been a hot research field
Classical reinforcement learning methods can be basically used in text classification, such as support vector machine, random forest, Naive Bayesian, neural network, and other algorithms
In the training process of the model, we use the grid search technique to find the optimal parameters of Naive Bayesian classifier support and the support vector machine classifier in this paper. e training parameters of each classifier are shown in Tables 2 and 3
Summary
In the field of natural language processing, emotion analysis has always been a hot research field. E traditional method of discriminating emotional polarity of text is based on machine learning since the 1990s. E first step is to construct the word vector feature manually to obtain the required text information. Classical reinforcement learning methods can be basically used in text classification, such as support vector machine, random forest, Naive Bayesian, neural network, and other algorithms. E word embedding model is an important research result that introduces deep learning algorithm into the field of natural language. Research on Text Classification Based on Support Vector Machine Model. Support vector machine learning algorithm is proposed, and it combines the features of words, parts of speech, and named entities for the text classification task with named entity elements which could achieve good results in the text classification task [6]. Different weights of each element in the text were presented in the mechanism in the LSTM model, and weights of each element in the text were iteratively updated through training in the LSTM model
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