Conversational sentiment analysis is an emerging, yet challenging subtask of the sentiment analysis problem. It aims to discover the affective state and sentimental change in each person in a conversation based on their opinions. There exists a wealth of interaction information that affects speaker sentiment in conversations. However, existing sentiment analysis approaches are insufficient in dealing with this subtask due to two primary reasons: the lack of benchmark conversational sentiment datasets and the inability to model interactions between individuals. To address these issues, in this paper, we first present a new conversational dataset that we created and made publicly available, named ScenarioSA, to support the development of conversational sentiment analysis models. Then, we investigate how interaction dynamics are associated with conversations and study the multidimensional nature of interactions, which is understandability, credibility and influence. Finally, we propose an interactive long short-term memory (LSTM) network for conversational sentiment analysis to model interactions between speakers in a conversation by (1) adding a confidence gate before each LSTM hidden unit to estimate the credibility of the previous speakers and (2) combining the output gate with the learned influence scores to incorporate the influences of the previous speakers. Extensive experiments are conducted on ScenarioSA and IEMOCAP, and the results show that our model outperforms a wide range of strong baselines and achieves competitive results with the state-of-art approaches.

会话情感分析是情感分析问题的一个新兴但具有挑战性的子任务。它旨在根据他们的意见发现每个人在谈话中的情感状态和情感变化。存在大量的交互信息，这些信息会影响对话中的讲话者情绪。但是，由于两个主要原因，现有的情感分析方法不足以处理此子任务：缺乏基准的会话情感数据集以及无法对个人之间的交互进行建模。为了解决这些问题，在本文中，我们首先介绍一个我们创建并公开可用的新会话数据集，名为ScenarioSA，以支持会话情感分析模型的开发。然后，我们研究了互动动力与对话的关系，并研究了互动的多维性质，即可理解性，可信度和影响力。最后，我们提出了一种用于对话情绪分析的交互式长短期记忆（LSTM）网络，以通过以下方式对说话者之间的交互进行建模：（1）在每个LSTM隐藏单元之前添加一个置信度门以估计先前说话者的可信度，以及（ 2）将输出门与学习到的影响力分数相结合，以合并先前说话者的影响力。在ScenarioSA和IEMOCAP上进行了广泛的实验，结果表明我们的模型优于广泛的强基准，并且使用最新方法获得了竞争性结果。