Emotion Recognition in Sentences - A Recurrent Neural Network Approach - Computational Intelligence in Data Science Access content directly
Conference Papers Year : 2020

Emotion Recognition in Sentences - A Recurrent Neural Network Approach

N. S. Kumar
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Mahathi Amencherla
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Manu George Vimal
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This paper presents a Long short-term memory (LSTM) model to automate the tagging of sentences with a dominant emotion. LSTM is a type of artificial recurrent neural network (RNN) architecture. A labeled corpus of news headlinesc [1] and textual descriptions [3] are used to train our model. The data set is annotated for a set of six basic emotions: Joy, Sadness, Fear, Anger, Disgust and Surprise and also takes into consideration a 7$$^\mathrm{th}$$th emotion, Neutral, to adequately represent sentences with incongruous emotions. Our model takes into account that one sentence can represent a conjunct of emotions and resolves all such conflicts to bring out one dominant emotion that the sentence can be categorized into. Furthermore, this can be extended to categorize an entire paragraph into a particular emotion. Our model gives us an accuracy of 85.63% for the prediction of emotion when trained on the above mentioned data set and an accuracy of 91.6% for the prediction of degree of emotion for a sentence. Additionally, every sentence is associated with a degree of the dominant emotion. One can infer that a degree of emotion means the extent of the emphasis of an emotion. Although, more than one sentence conveys the same emotion, the amount of emphasis of the emotion itself can vary depending on the context. This feature of determining the emphasis of an emotion, that is, degree of an emotion, is also taken care of in our model.
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Dates and versions

hal-03434798 , version 1 (18-11-2021)





N. S. Kumar, Mahathi Amencherla, Manu George Vimal. Emotion Recognition in Sentences - A Recurrent Neural Network Approach. 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.3-15, ⟨10.1007/978-3-030-63467-4_1⟩. ⟨hal-03434798⟩
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