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• W3202775307 abstract "• We propose a fast, compact and parameter-efficient party-ignorant framework based on emotional recurrent unit. • We design generalized neural tensor block which is suitable for different structures, to perform context compositionality. • Experiments on three standard benchmarks indicate that our model outperforms the state of the art with fewer parameters. 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." @default.
• W3202775307 created "2021-10-11" @default.
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• W3202775307 date "2022-01-01" @default.
• W3202775307 modified "2023-10-18" @default.
• W3202775307 title "BiERU: Bidirectional emotional recurrent unit for conversational sentiment analysis" @default.
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• W3202775307 doi "https://doi.org/10.1016/j.neucom.2021.09.057" @default.
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