关键词: 文本情感分类, textSE-ResNeXt, 特征划分, 集成模型

Abstract:

Aiming at the deep learning method that the text representation is single, and difficult to effectively use the defects of the refined features between the corpus. For the different characteristics between the Chinese and English corpora, a new type of textSE-ResNeXt integration model is proposed by distinguishing the characteristics of Chinese and English corpus. Through the PDTB corpus, the explicit relationship of the corpus is analyzed, so that the main emotional part of the corpus is intercepted. The emotional degree relationship is divided according to different Chinese and English sentiment lexicons, and sub-data sets with different levels of emotion are gotten. In the textSE-ResNeXt neural network model, the dynamic convolution kernel strategy is adopted to extract the text data features more effectively. The model incorporates SEnet and ResNeXt, which effectively extracts and classifies deep text features. The subset of different emotion levels is used to further improve the classification efficiency by adopting the voting integration method for the textSE-ResNeXt model. Experiments are conducted on Chinese hotel commentary corpus and six common English classification data sets. The experimental results show the effectiveness of the model.

Key words: text sentiment classification, textSE-ResNeXt, feature division, integrated model