Understanding Emotions in Text Using Deep Learning and Big Data

Chatterjee, Ankush; Gupta, Umang; Chinnakotla, Manoj Kumar; Srikanth, Radhakrishnan; Galley, Michel; Agrawal, Puneet

doi:10.1016/j.chb.2018.12.029

Cited by 277 publications

(163 citation statements)

References 14 publications

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“…The need to consider the context of the conversion is essential in this case, even for human, specifically given the lack of voice modulation and facial expressions. The usage of figurative language, like sarcasm, and the class size's imbalance adds up to this problematic (Chatterjee et al, 2019a). In this paper, we describe our model, which was proposed for the SemEval 2019-Task 3 competition: Contextual Emotion Detection in Text (Emo-Context).…”

Section: Introductionmentioning

confidence: 99%

ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT

Huang¹,

Trabelsi²,

Zaı̈ane³

2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchical LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational context. The results show that, in this task, our HRCLE outperforms the most recent state-ofthe-art text classification framework: BERT. We combine the results generated by BERT and HRCLE to achieve an overall score of 0.7709 which ranked 5th on the final leader board of the competition among 165 Teams.

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Section: Introductionmentioning

confidence: 99%

ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT

Huang¹,

Trabelsi²,

Zaı̈ane³

2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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“…EmoContext shared task has garnered more than 500 participants, affirming the growing popularity of this research 1 https://www.humanizing-ai.com/emocontext.html field. Compared to other datasets, EmoContext dataset [37] has very short conversations consisting only three utterances where the goal is to label the 3rd utterance as shown in Fig. 10.…”

Section: Iemocapmentioning

confidence: 99%

“…Emotion labels of the previous utterances are not present in the EmoContext dataset. The key works [24,39,37] on this dataset have mainly leveraged on context modeling using bc-LSTM architecture [31] that encapsulates the temporal order of the utterances using an LSTM. A common trend can be noticed in these works, where traditional word embeddings, such as Glove [40], are combined with contextualized word embeddings, such as ELMo [29] to improve the performance.…”

Section: Iemocapmentioning

confidence: 99%

Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

Majumder²,

et al. 2019

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Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data in platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration) and more. Additionally, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user's emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a strenuous problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on the recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC.

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“…The majority of existing works focused on the multi-modality settings (Devillers et al, 2002;Hazarika et al, 2018;Majumder et al, 2019). Chatterjee et al (2019a) is one of the early works on the textual modality that first collected the dataset used in this task and then proposed an LSTM model with both semantic and sentiment embeddings to classify emotions. This task is also closely related to sentiment analysis (Pang et al, 2008) where the opinions of a piece of text is to be identified.…”

Section: Related Workmentioning

confidence: 99%

ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN

Zhong¹,

Chen²

2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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In this paper we present our model on the task of emotion detection in textual conversations in SemEval-2019. Our model extends the Recurrent Convolutional Neural Network (RCNN) by using external fine-tuned word representations and DeepMoji sentence representations. We also explored several other competitive pre-trained word and sentence representations including ELMo, BERT and InferSent but found inferior performance. In addition, we conducted extensive sensitivity analysis, which empirically shows that our model is relatively robust to hyper-parameters. Our model requires no handcrafted features or emotion lexicons but achieved good performance with a micro-F1 score of 0.7463.

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Understanding Emotions in Text Using Deep Learning and Big Data

Cited by 277 publications

References 14 publications

ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT

ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT

Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN

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