Abstract
In this paper, we propose a novel neural network based architecture which incorporates character, word and lexicon level information to predict the degree of intensity for sentiment and emotion. At first we develop two deep learning models based on Long Short Term Memory (LSTM) & Convolutional Neural Network (CNN), and a feature based model. Each of these models takes as input a fusion of various representations obtained from the characters, words and lexicons. A Multi-Layer Perceptron (MLP) network based ensemble model is then constructed by combining the outputs of these three models. Evaluation on the benchmark datasets related to sentiment and emotion shows that our proposed model attains the state-of-the-art performance.
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Notes
- 1.
We also call this problem as fine-grained emotion & sentiment analysis.
- 2.
Spell Checker Oriented Word Lists (SCOWL): http://wordlist.aspell.net/.
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Acknowledgments
Asif Ekbal acknowledges Young Faculty Research Fellowship (YFRF), supported by Visvesvaraya PhD scheme for Electronics and IT, Ministry of Electronics and Information Technology (MeitY), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia).
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Ghosal, D., Akhtar, M.S., Ekbal, A., Bhattacharyya, P. (2018). Deep Ensemble Model with the Fusion of Character, Word and Lexicon Level Information for Emotion and Sentiment Prediction. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_15
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