Self-Attentive Sentimental Sentence Embedding for Sentiment Analysis

Sheng-Chieh Lin, Wen-Yuh Su, Po-Chuan Chien, Ming-Feng Tsai, Chuan-Ju Wang

Abstract: We propose the use of a word-level sentiment bidirectional LSTM in tandem with the self-attention mechanism for sentence-level sentiment prediction. In addition to the proposed model, we also present a finance report dataset for sentence-level financial risk detection. Experiments conducted on the proposed dataset together with two public review datasets attest the effectiveness of our model for sentence sentiment prediction.