Deep Learning Sentiment Classification Based on Weak Tagging Information

doi:10.1109/ACCESS.2021.3077059

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Deep Learning Sentiment Classification Based on Weak Tagging Information

2021-01-01

Chuantao Wang

Yang Xuexin *

Linkai Ding

DOI：10.1109/ACCESS.2021.3077059

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Abstract

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The purpose of sentiment classification is to solve the problem of automatic judgment of text sentiment tendency. In the sentiment classification task of online reviews, traditional deep learning sentiment classification models focus on algorithm optimization to improve the classification performance of the model, but when the sample data for manually labeling sentiment tendencies is insufficient, the classification performance of the model will be poor. The deep learning sentiment classification model based on weak tagging information, on the one hand, introduces weak tagging information into the training process of the model to reduce the use of manually tagging data. On the other hand, weak tagging information can represent the sentiment tendency of reviews to a certain extent, but it also contains noise, the model reduces the negative impact of the noise in weak tagging information in order to improve the classification performance of the sentiment classification model. The experimental results show that in the sentiment classification task of hotel online reviews, the deep learning sentiment classification model based on weak tagging information has superior classification performance than the traditional deep model without increasing labor cost.

Keywords:

Tagging

Data models

Task analysis

Deep learning

Dictionaries

Training

Cleaning

Deep learning

sentiment classification

weak tagging information

imbalanced classification

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