Modeling Rich Contexts for Sentiment Classification with LSTM

2016/05/05 by Minlie Huang, Yujie Cao, Huang, Minlie +3
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI) #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1605.01478

openalex publication_date 2016/05/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Sentiment analysis on social media data such as tweets and weibo has become a very important and challenging task. Due to the intrinsic properties of such data, tweets are short, noisy, and of divergent topics, and sentiment classification on these data requires to modeling various contexts such as the retweet/reply history of a tweet, and the social context about authors and relationships. While few prior study has approached the issue of modeling contexts in tweet, this paper proposes to use a hierarchical LSTM to model rich contexts in tweet, particularly long-range context. Experimental results show that contexts can help us to perform sentiment classification remarkably better.

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