article · Computational Intelligence and Neuroscience
Emotion detection aims to identify specific emotions from text, moving beyond simple positive, negative, or neutral categorisations. Unlike speech or facial expression analysis, text analysis lacks tonal, pitch, and visual cues, making the identification of emotional nuance challenging. Traditional methods using keyword and lexicon-based techniques have faced constraints because of their reliance on semantic relations. To address these difficulties, a hybrid framework combines deep learning and machine learning architectures. This configuration brings together convolutional neural networks, bidirectional gated recurrent units, and support vector machines to capture complex textual patterns. When evaluated across a combined benchmark consisting of sentences, social media tweets, and dialogues, the integrated architecture achieved an overall accuracy rate of 80.11 per cent.
Identifying specific emotions in written communication is notoriously difficult because text lacks vocal tone and facial expressions. By combining complementary machine learning and deep learning techniques, systems can more accurately interpret subtle emotional states in written content. This enhances how automated tools interpret complex language across everyday communication formats such as social media posts and conversational dialogues.
The abstract does not specify an explicit commercialisation pathway or intended commercial users. The architecture represents early-stage technical research evaluated on benchmark datasets of sentences, tweets, and dialogues. Potential applications could include automated analysis of consumer sentiments, social media monitoring, and customer service dialogue processing, but real-world deployment would require further applied testing and integration into functional software platforms.
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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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DOI: 10.1155/2022/2645381
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