Attentional Neural Network for Emotion Detection in Conversations with Speaker Influence Awareness

Wei, Jun; Feng, Shi; Wang, Daling; Zhang, Yifei; Li, Xiangju

doi:10.1007/978-3-030-32236-6_25

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…The work was supported by the National Key R&D Program of China under Grant 2018YFB1004700, National Natural Science Foundation of China (61872074, 61772122), and the Fundamental Research Funds for the Central Universities (N180716010). This paper is a substantial extension of our previous work in [48]. In this paper, a large-scale Chinese dialog dataset WBEmoDialog is constructed, and the proposed SINN model is evaluated on the new dataset.…”

Section: Acknowledgementsmentioning

confidence: 97%

SINN: A speaker influence aware neural network model for emotion detection in conversations

et al. 2021

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Inferring the sentiment polarity or emotion category of subjective text is the fundamental task of sentiment analysis. Recently, emotion detection in conversations that considering context utterances has emerged as a very important and challenging task in this line of research. Most existing studies do not distinguish different speakers in a dialog and fail to characterize inter-speaker dependencies for emotion detection. In this paper, we propose a Speaker Influence aware Neural Network model (dubbed as SINN) to predict the emotion of the last utterance in a conversation, which explicitly models the self and inter-speaker influences of historical utterances with GRUs (Gated Recurrent Units) and hierarchical attention matching network. Moreover, the empathy phenomenon is also considered by an emotion state tracking component in SINN. Finally, the target utterance representation is enhanced by speaker influence aware context modeling, where an attention mechanism is used to extract the most relevant features for emotion classification. We construct a largescale multi-turn Chinese dialog dataset WBEmoDialog, where each utterance is manually annotated with an emotion label. Extensive experiments are conducted on public available DailyDialog dataset as well as our constructed WBEmoDialog dataset, and the results show that our model can achieve better or comparable performance with the strong baseline methods.

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Section: Acknowledgementsmentioning

confidence: 97%

SINN: A speaker influence aware neural network model for emotion detection in conversations

et al. 2021

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“…Conversational sentiment analysis differs from sentence-level and chapter-level sentiment analysis in that it needs to consider the contextual background of the conversation and the affective interactions between different speakers [6,8]. Therefore, contextual relevance models and speaker relevance models are two entry points for conversational sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

“…Recognizing customer sentiment in conversations is very dependent on the contextual semantic information of the conversation or the affective interaction information between the speakers [5,6]. Take the conversation in Figure 1 as an example, if only considering the sentiment of the current utterance, the third utterance expresses a neutral sentiment.…”

Section: Introductionmentioning

confidence: 99%

Customer Sentiment Recognition in Conversation Based on Contextual Semantic and Affective Interaction Information

Huang

Liu

Zhu³

et al. 2023

Applied Sciences

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In the e-commerce environment, conversations between customers and businesses contain a multitude of useful information about customer sentiment. By mining that information, customer sentiment can be validly identified, which is helpful in accurately identifying customer needs and improving customer satisfaction. Contextual semantics information and inter-speaker affective interaction information are two key factors for identifying customers’ sentiments in conversation. Unfortunately, none of the existing approaches consider the two factors simultaneously. In this paper, we propose a conversational sentiment analysis method based on contextual semantic and affective interaction information. The proposed approach uses different bidirectional gated recurrent unit (BiGRU) combined with attention mechanisms to encode the contextual semantic information of different types of conversational texts. For modeling affective interactions, we use directed graph structures to portray the affective interactions between speakers and encode them with affective interaction features using graph convolutional neural networks (GCN). Finally, the two features are afused to recognize customer sentiment. The experimental results on the JDDC dataset show that our model can more accurately recognize customer sentiment than other baseline models in customer service conversation.

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AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions

et al. 2022

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Artificial intelligence is changing the world, especially the interaction between machines and humans. Learning and interpreting natural languages and responding have paved the way for many technologies and applications. The amalgam of machine learning, deep learning, and natural language processing helped Conversational Artificial Intelligence (AI) to change the face of Human-Computer Interaction (HCI). A conversational agent is an excellent example of conversational AI, which imitates the natural language. This article presents a sweeping overview of conversational agents that includes different techniques such as pattern-based, machine learning, and deep learning used to implement conversational agents. It also discusses the panorama of different tasks in conversational agents. This study also focuses on how conversational agents can simulate human behavior by adding emotions, sentiments, and affect to the context. With the advancements in recent trends and the rise in deep learning models, the authors review the deep learning techniques and various publicly available datasets used in conversational agents. This article unearths the research gaps in conversational agents and gives insights into future directions.

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Attentional Neural Network for Emotion Detection in Conversations with Speaker Influence Awareness

Cited by 5 publications

References 8 publications

SINN: A speaker influence aware neural network model for emotion detection in conversations

SINN: A speaker influence aware neural network model for emotion detection in conversations

Customer Sentiment Recognition in Conversation Based on Contextual Semantic and Affective Interaction Information

AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions

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