A Parallel Two-Channel Emotion Classification Method for Chinese Text

The complex structure of Chinese, including the dual-granularity feature of words and phrases, poses unique challenges for sentiment analysis, which is significantly different from the alphabetic word formation mechanism of English text. To capture Chinese semantics more accurately, an advanced parallel channel sentiment classification strategy is designed. In this study, the advanced pre-training model ERNIE with Word2vec is first adopted to enrich the embedding of Chinese text at word granularity. Following that, fine-grained features at the word level are extracted by a multi-window convolutional strategy, and the word vector sequences are deeply learnt using BiGRU network to ensure the comprehensive capture of contextual information in both directions. To further optimise the model, an Attention mechanism is introduced to ensure effective delivery of information and improve computational efficiency. After this series of innovative designs, the microblog comment dataset is used as an experimental case, and the hyperparameters are adjusted to determine the optimal parameters. Six comparison models are selected to verify the effectiveness of MsCBA on three datasets. The classification accuracies of the proposed model on the three datasets are 93.64%, 90.00% and 92.61%, respectively, which are better than the comparison models. This study provides an efficient and innovative approach for Chinese sentiment analysis, which sheds new light on the field of NLP.