  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 which 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.
