Emoji-based Co-attention Network for Microblog Sentiment Analysis
Emojis are widely used in online social networks to express emotions, attitudes, and opinions. As emotional-oriented characters, emojis can be modeled as important features of emotions towards the recipient or subject for sentiment analysis. However, existing methods mainly take emojis as heuristic information that fails to resolve the problem of ambiguity noise. Recent researches have utilized emojis as an independent input to classify text sentiment but they ignore the emotional impact of the interaction between text and emojis. It results that the emotional semantics of emojis cannot be fully explored. In this paper, we propose an emoji-based co-attention network that learns the mutual emotional semantics between text and emojis on microblogs. Our model adopts the co-attention mechanism based on bidirectional long short-term memory incorporating the text and emojis, and integrates a squeeze-and-excitation block in a convolutional neural network classifier to increase its sensitivity to emotional semantic features. Experimental results show that the proposed method can significantly outperform several baselines for sentiment analysis on short texts of social media.
Code (0)
등록된 구현이 없습니다.
Tasks
Sentiment AnalysisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Emoji-based Fine-grained Attention Network for Sentiment Analysis in the Microblog Comments
Microblogs have become a social platform for people to express their emotions in real-time, and it is a trend to analyze user emotional tendencies from the information on Microblogs. The dynamic features of emojis can af…
DiversitySentiment AnalysisSentiment ClassificationTwitter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction
In this paper, we leverage social media platforms such as twitter for developing corpus across multiple languages. The corpus creation methodology is applicable for resource-scarce languages provided the speakers of that…
Sentiment AnalysisTwitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM
Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status …
Sentiment AnalysisTwitter Sentiment AnalysisPay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysis
With the explosive growth of social media, opinionated postings with emojis have increased explosively. Many emojis are used to express emotions, attitudes, and opinions. Emoji representation learning can be helpful to i…
Representation LearningSentiment AnalysisTransfer LearningA new ANEW: Evaluation of a word list for sentiment analysis in microblogs
Sentiment analysis of microblogs such as Twitter has recently gained a fair amount of attention. One of the simplest sentiment analysis approaches compares the words of a posting against a labeled word list, where each w…
Sentiment Analysis