ALBERTC-CNN Based Aspect Level Sentiment Analysis

In order to solve the problem that most aspect level sentiment analysis networks cannot extract the global and local information of the context at the same time. This study proposes an aspect level sentiment analysis model named Combining with A Lite Bidirection Encoder Represention from TransConvs and ConvNets(ALBERTC-CNN). First, the global sentence information and local emotion information in a text are extracted by the improved ALBERTC network, and the input aspect level text is represented by a word vector. Then, the feature vector is mapped to the emotion classification number by a linear function and a softmax function. Finally, the aspect level sentiment analysis results are obtained. The proposed model is tested on two datasets of the SemEval-2014 open task, the laptop and restaurant datasets, and compared with the traditional networks. The results show that compared with the traditional network, the classification accuracy of the proposed model is improved by approximately 4% and 5% on the two sets, whereas the F1 value is improved by approximately 4% and 8%. Additionally, compared with the original ALBERT network, the accuracy is improved by approximately 2%, and the F1 value is improved by approximately 1%.

Automated summary

This study introduces a new aspect-level sentiment analysis model named ALBERTC-CNN, which combines different network advantages to improve emotion classification accuracy. The model is tested on two datasets and achieves promising results compared with traditional networks.