BiERU: Bidirectional emotional recurrent unit for conversational sentiment analysis

Wei Li, Wei Shao, Shaoxiong Ji, Erik Cambria*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e.g., sentiment analysis, recommender systems, and human-robot interaction. The main difference between conversational sentiment analysis and single sentence sentiment analysis is the existence of context information that may influence the sentiment of an utterance in a dialogue. How to effectively encode contextual information in dialogues, however, remains a challenge. Existing approaches employ complicated deep learning structures to distinguish different parties in a conversation and then model the context information. In this paper, we propose a fast, compact and parameter-efficient party-ignorant framework named bidirectional emotional recurrent unit for conversational sentiment analysis. In our system, a generalized neural tensor block followed by a two-channel classifier is designed to perform context compositionality and sentiment classification, respectively. Extensive experiments on three standard datasets demonstrate that our model outperforms the state of the art in most cases.

Original languageEnglish
Pages (from-to)73-82
Number of pages10
JournalNeurocomputing
Volume467
DOIs
Publication statusPublished - 7 Jan 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Contextual encoding
  • Conversational sentiment analysis
  • Dialogue systems
  • Emotional recurrent unit

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