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Context-Aware Text Representation for Social Relation Aided Sentiment Analysis

In this paper, we propose CaTER, which learns a novel context-aware joint representation of text and user by incorporating semantic text embedding of unlabeled tweets as well as social relation information. CaTER leverages the wealth of user contextual information available apart from user's utterances for sentiment analysis. Our approach is inspired by social science about emotional behaviors of connected users, who perhaps more likely to consensus on similar opinions. Our method outperforms numerous baselines on two real-world Twitter datasets.