Year: 2020
Type: article
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.
Source: Complexity
DOI: 10.1155/2020/5093620
Authors: Ruixia Yan, Zhijie Xia, Yanxi Xie, Xiaoli Wang, Zukang Song
Institution: Shanghai University of Engineering Science
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,760
OpenAlex ID: W3083348993