TaneNet: Two-Level Attention Network Based on Emojis for Sentiment Analysis

Qin Zhao, Peihan Wu, Jie Lian, Dongdong An, Maozhen Li

Abstract: During online communication, users often use irregular and ambiguous words, and sometimes use irony to express sarcasm. These words are difficult to analyze through text analysis, which poses a significant challenge for text sentiment analysis. As a novel communication method, emojis have a significant correlation with user emotions. In this paper, we use emojis to analyze the sentiment of short texts. Firstly, we validate that user information can help reduce the uncertainty of some emojis and use this information to identify the polarity of emojis. Then, we generate emoji representations by merging positional information, semantic information, emotional information, and frequency of appearance. Furthermore, we propose TaneNet, a two-level attention network based on emojis, which combines clause vectors and emoji representations to study the impact of emojis on the emotions of each clause in the text. Empirical results on two real-world datasets demonstrate that TaneNet outperforms existing state-of-the-art methods.