ArticleComputational intelligence and neuroscience2022
Text-Based Emotion Recognition Using Deep Learning Approach.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- A hybrid stacked ensemble learning framework for multilabel text emotion detection.Scientific reports · 2026Article
- Data-centric review of multimodal emotion recognition: datasets, feature extraction, data fusion and limitations.Frontiers in robotics and AI · 2026Review
- A machine learning based empathy mapping framework for enhancing user experience through app review analysis.Scientific reports · 2025Article
- Integration of corpus linguistics and deep learning techniques for enhanced semantic-driven emotion detection on textual data.Scientific reports · 2025Article
- Textual emotion recognition to improve real-time communication of disabled people in sustainable environments using an ensemble deep learning approach.Scientific reports · 2025Article
- Empowering people with intellectual disabilities using integrated deep learning architecture driven enhanced text-based emotion classification.Scientific reports · 2025Article
- Analyzing social psychological impact on emotional expression through peer communication using crayfish optimization algorithm with deep learning model.Scientific reports · 2025Article
- Contextualizing injury severity from occupational accident reports using an optimized deep learning prediction model.PeerJ. Computer science · 2024Article
- EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures.Sensors (Basel, Switzerland) · 2023Article
- An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method.Frontiers in neuroscience · 2023Article
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7 authors.
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Abstract
Sentiment analysis is a method to identify people's attitudes, sentiments, and emotions towards a given goal, such as people, activities, organizations, services, subjects, and products. Emotion detection is a subset of sentiment analysis as it predicts the unique emotion rather than just stating positive, negative, or neutral. In recent times, many researchers have already worked on speech and facial expressions for emotion recognition. However, emotion detection in text is a tedious task as cues are missing, unlike in speech, such as tonal stress, facial expression, pitch, etc. To identify emotions from text, several methods have been proposed in the past using natural language processing (NLP) techniques: the keyword approach, the lexicon-based approach, and the machine learning approach. However, there were some limitations with keyword- and lexicon-based approaches as they focus on semantic relations. In this article, we have proposed a hybrid (machine learning + deep learning) model to identify emotions in text. Convolutional neural network (CNN) and Bi-GRU were exploited as deep learning techniques. Support vector machine is used as a machine learning approach. The performance of the proposed approach is evaluated using a combination of three different types of datasets, namely, sentences, tweets, and dialogs, and it attains an accuracy of 80.11%.
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