以注入情緒構面的雙向長短期記憶模型應用於社群媒體產品評論之情感分析

Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Social Media Product Reviews

現今是網際網路普及的時代，社群媒體評台擔任私人企業與大眾間相互溝通的橋樑角色。本研究之目的是透過解析不同領域的產品評論資料獲取用戶對於產品的心得。我們提出一個具有更細膩詞彙情感資訊的BiLSTM(Bi-directional Long-Short Term Memory)模型，不只能預測文字中最小詞素「詞彙」的情緒構面Valence & Arousal，也能加入詞彙間的依存關係。實驗結果顯示，本研究在預測詞彙之Valence & Arousal可以達到良好的效能。而且，經由融合VA(Valence & Arousal)及詞彙間依存關係至BiLATM模型更可以對於社群文字情感分析坐出優異表現，驗證此模型對於產品評論留言的情感預測準確性。

With the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research is to understand the user’s experience of the product by analyzing product review data in different fields. We propose a BiLSTM-based neural network which infused rich emotional information. In addition to consider Valence and Arousal which is the smallest morpheme of emotional information, the dependence relationship between texts is also integrated into the deep learning model to analyze the sentiment. The experimental results show that this research can achieve good performance in predicting the vocabulary Valence and Arousal. In addition, the integration of VA and dependency information into the BiLSTM model can have excellent performance for social text sentiment analysis, which verifies that this model is effective in emotion recognition of social medial short text.