Currently, various attention-based neural networks have achieved successes in sentiment classification tasks, as attention mechanism is capable of focusing on those words contributing more to the sentiment polarity prediction than others. However, the major drawback of these approaches is that they only pay attention to the words, the sentimental information contained in the part-of-speech(POS) is ignored. To address this problem, in this paper, we propose Part-of-Speech based Transformer Attention Network(pos-TAN). This model not only uses the Self-Attention mechanism to learn the feature expression of the text but also incorporates the POS-Attention, which uses to capture sentimental information contained in part-of-speech. In addition, our innovative introduction of the Focal Loss effectively alleviates the impact of sample imbalance on model performance. We conduct substantial experiments on various datasets, and the encouraging results indicate the efficacy of our proposed approach.

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In view of the deficiency of domestic Weibo user attribute analysis, the imperfection of Weibo feature extraction and the problem that the classification accuracy needs to be improved, a Weibo user attribute analysis method based on multi-features is proposed. This paper first uses the word2vec model to build text features from Weibo content, then constructs Weibo user features from Weibo information and user information, and finally sends multi-feature sets into the improved three-tier stacking model to build Weibo user attribute analysis model. The experimental results show that this method has better classification effect than the text-based classification method and the traditional stacking model.