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Title: How intense are you? Predicting intensities of emotions and sentiments using stacked ensemble [application notes]
Authors: Akhtar, M. S.
Ekbal, A.
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Akhtar, M. S., Ekbal, A. & Cambria, E. (2020). How intense are you? Predicting intensities of emotions and sentiments using stacked ensemble [application notes]. IEEE Computational Intelligence Magazine, 15(1), 64-75.
Journal: IEEE Computational Intelligence Magazine
Abstract: Emotions and sentiments are subjective in nature. They differ on a case-to-case basis. However,-predicting only the emotion and sentiment does not always convey complete information. The degree or level of emotions and sentiments often plays a crucial role in understanding the exact feeling within a single class (e.g., 'good' versus 'awesome'). In this paper, we propose a stacked ensemble method for predicting the degree of intensity for emotion and sentiment by combining the outputs obtained from several deep learning and classical feature-based models using a multi-layer perceptron network. We develop three deep learning models based on convolutional neural network, long short-term memory and gated recurrent unit and one classical supervised model based on support vector regression. We evaluate our proposed technique for two problems, i.e., emotion analysis in the generic domain and sentiment analysis in the financial domain. The proposed model shows impressive results for both the problems. Comparisons show that our proposed model achieves improved performance over the existing state-of-theart systems.
ISSN: 1556-603X
DOI: 10.1109/MCI.2019.2954667
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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