LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification

Ragheb, Waleed; Azé, Jérôme; Bringay, Sandra; Servajean, Maximilien

doi:10.18653/v1/s19-2042

Cited by 7 publications

(3 citation statements)

References 11 publications

(8 reference statements)

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“…Ragheb et al 81 presented an attention‐based model for detecting and classifying emotions in textual conversations. The data used was provided by the SemEval‐2019 Task 3 competition organizers.…”

Section: Detection Approaches and Related Workmentioning

confidence: 99%

Text‐based emotion detection: Advances, challenges, and opportunities

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

220

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Emotion detection (ED) is a branch of sentiment analysis that deals with the extraction and analysis of emotions. The evolution of Web 2.0 has put text mining and analysis at the frontiers of organizational success. It helps service providers provide tailor-made services to their customers. Numerous studies are being carried out in the area of text mining and analysis due to the ease in sourcing for data and the vast benefits its deliverable offers. This article surveys the concept of ED from texts and highlights the main approaches adopted by researchers in the design of text-based ED systems. The article further discusses some recent state-of-the-art proposals in the field. The proposals are discussed in relation to their major contributions, approaches employed, datasets used, results obtained, strengths, and their weaknesses. Also, emotion-labeled data sources are presented to provide neophytes with eligible text datasets for ED. Finally, the article presents some open issues and future research direction for text-based ED. K E Y W O R D S emotion detection, natural language processing, sentiment analysis, text-based emotion detection This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Detection Approaches and Related Workmentioning

confidence: 99%

Text‐based emotion detection: Advances, challenges, and opportunities

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

220

View full text Add to dashboard Cite

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“…Additionally, numerous research studies have explored the application of deep learning models for text-based emotion analysis. Ragheb et al [21] used Bidirectional-Long Short-Term Memory units to detect emotions in textual conversations. Keshavarz & Abadeh [22] utilized a CNN model and achieved significant improvement compared to traditional machine learning approaches.…”

Section: Related Workmentioning

confidence: 99%

Emotion-Based Movie Recommendation System

Tennakoon,

Senaweera,

Dharmarathne

2024

Int J on Adv. in ICT for Emerging Countries

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This study presents a novel approach for a movie recommendation system that uses the emotions of a user to recommend movies. To detect user emotions, the system uses both facial expressions and text analysis. To detect facial expressions, several types of pre-trained models were re-trained and evaluated using benchmark datasets (FER2013). The ResNet50 model which has the highest accuracy of 73% was selected as the final model. For text analysis, several classical machine learning models (SVM, RF, MNB) and deep learning models (LSTM, Bi-LSTM, BERT, BERT+CNN) were trained and evaluated for their effectiveness in classifying emotions (using ISEAR). The BERT+CNN model with an accuracy of 78% was ultimately chosen for its high accuracy and efficiency in handling textual data. Final emotion derived by applying soft voting ensemble technique to the results of facial expression model and the text analysis model.For making the recommendations, the study incorporated content-based and collaborative filtering techniques to recommend movies based on the users’ emotional state. Both methods were combined and adjusted based on the user’s emotional state, resulting in more personalized movie recommendations.To assess the efficiency of the proposed system, feedback was collected from ten users and analyzed. The final system received positive feedback from seven of the ten users. This indicated that the proposed system has the potential to enhance user experience by providing more personalized and relevant movie recommendations based on their emotional state.

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“…The BERT-CNN performance is compared to performances of the state-of-the-art models using two datasets mentioned in the previous sections. These models are Emotdet [16], EMODET 2 [14], Nture [17], SCIA [18], Coastal [15], PKUSE [19], EPITA-ADAPT [20], Figure Eight [21], NELEC [22], THU NGN [23], LIRMM [24], NTUA-ISLab [25], Syman to Research [26], ANA [27], CAiRE-HKUST [28], GenSMT [29], SNU_IDS [30], CLARK [31], and SINAI [32].…”

Section: Compared Algorithmsmentioning

confidence: 99%

BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

Abas¹,

El-Henawy²,

Zidan³

et al. 2022

Computers, Materials &Amp; Continua

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Due to the widespread usage of social media in our recent daily lifestyles, sentiment analysis becomes an important field in pattern recognition and Natural Language Processing (NLP). In this field, users' feedback data on a specific issue are evaluated and analyzed. Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research. Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature. Emotions describe a state of mind of distinct behaviors, feelings, thoughts and experiences. The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text. This model is formed by a combination of the Bidirectional Encoder Representations from Transformer (BERT) and the Convolutional Neural networks (CNN) for textual classification. This model embraces the BERT to train the word semantic representation language model. According to the word context, the semantic vector is dynamically generated and then placed into the CNN to predict the output. Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets. The BERT-CNN model achieves an accuracy of 94.7% and an F1-score of 94% for semeval2019 task3 dataset and an accuracy of 75.8% and an F1-score of 76% for ISEAR dataset.

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LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification

Cited by 7 publications

References 11 publications

Text‐based emotion detection: Advances, challenges, and opportunities

Text‐based emotion detection: Advances, challenges, and opportunities

Emotion-Based Movie Recommendation System

BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

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