A Novel AB-CNN Model for Multi-Classification Sentiment Analysis of e-Commerce Comments

Abstract

Despite the success of dichotomous sentiment analysis, it does not encompass the various emotional colors of users in reality, which can be more plentiful than a mere positive or negative association. Moreover, the complexity and imbalanced nature of Chinese text presents a formidable obstacle to overcome. To address prior inadequacies, the three-classification method is employed and a novel AB-CNN model is proposed, incorporating an attention mechanism, BiLSTM, and a CNN. The proposed model was tested on a public e-commerce dataset and demonstrated a superior performance compared to existing classifiers. It utilizes a word vector model to extract features from sentences and vectorize them. The attention layer is used to calculate the weighted average attention of each text, and the relevant representation is obtained. BiLSTM is then employed to read the text information from both directions, further enhancing the emotional level. Finally, softmax is used to classify the emotional polarity.