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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Emotion Recognition in Textual Conversations |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2021 |
| Abstract Text | Emotion recognition in textual conversations(ERC) is an important natural language processing (NLP) task with applications in different fields, including data mining, e-learning, human–computer interaction, and psychology. Recognizing emotions in textual conversations is a difficult problem to solve due to lack of facial expressions and voice modulations. Different from the traditional non-conversational emotion detection, the model for ERC needs to be context-sensitive (understands the whole conversation rather than individual utterance) and speaker-sensitive (understands which utterance belongs to which speaker) [Jingye et al., 2020]. This thesis aims to contribute to research efforts in the field of affective computing and to provide a holistic analysis of text-based emotion recognition with a focus on deep neural network architectures, as deep learning has achieved major breakthroughs and state-of-the-art results for a large number of tasks in the field of Natural Language Processing [Torfi et al., 2020]. In this work, we have explored the latest state of art approaches for emotion detection in text and analyzed the underlying techniques and emotional models. Subsequently, we have implemented a hierarchical transformer model for emotion detection purposed by [Qingbiao et al., 2020], using Pytorch Lightning Framework1 which leverages contextual information from the conversation history. It is a transformer-based context- and speaker sensitive model for ERC and consists of two hierarchical transformers. The implementation2 utilizes a pretrained BERT model from HuggingFace3 Transformers library as the lower level transformer to generate local utterance representations, and feed them into another high-level transformer so that utterance representations could be sensitive to the global context of the conversation. During this work, we have conducted experiments on four dialog emotion datasets, Friends, EmotionPush, EmoryNLP and Semeval EmoContext. Additionally, we evaluated the model performance on the German translation of benchmarked datasets. Results demonstrate that the hierarchical transformer network emotion model obtains competitive results compared with the state-of-the-art methods and can effectively capture the context and speaker information in textual conversations. |
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