Combination feature vector and deep learning based sentiment classification method and device

Abstract

The invention discloses a combination feature vector and deep learning based sentiment classification method and device. The combination feature vector and deep learning based sentiment classification method comprises the steps of obtaining multiple comment texts from internet; carrying out word segmentation for the multiple comment texts so as to obtain each sub-constituent words; obtaining a lexical feature of a sentence; extracting a syntactic feature of each comment text; obtaining a combination feature vector of each user comment text based on the lexical features and the syntactic features; and training a deep learning model based on the combination feature vector and further obtaining an optimal classification result through the deep learning model. According to the combination feature vector and deep learning based sentiment classification method and device, the comment text can be subjected to sentiment classification through the combination feature vector and the deep learning, the optimal classification result can be accordingly obtained, the classification precision is improved, and emotional tendency of a user in the text can be better identified; and the combination feature vector and deep learning based sentiment classification method is simple and convenient.

Description

technical field [0001] The invention relates to the technical fields of computer and Internet, in particular to a method and device for emotion classification based on combined feature vectors and deep learning. Background technique [0002] With the continuous development of the Internet and Web 2.0, e-commerce has become an important shopping channel that is indispensable in people's daily life. On the e-commerce website, buyers can comment on the purchased items and express their opinions and satisfaction, and these comments often contain emotional factors, including the buyer's attitude. By analyzing the purchase comments published by users, the user's emotional tendency can be classified. All e-commerce companies are also fully aware of the important value of this part of data information, and strive to obtain more accurate and effective data to provide data support for future decision-making. However, the transaction volume of the e-commerce platform can reach tens o...

AI Technical Summary

Problems solved by technology

However, the transaction volume of the e-commerce platform can reach tens of thousands every day, resulting in a huge amount of comment information, which is also difficult to be processed by humans in a timely and effective manner, and it is time-consuming and labor-intensive.

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