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Text-based emotion recognition using contextual phrase embedding model

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Abstract

In this paper, the proposed approach categories the sentences in the dataset into the various topical documents using the TE-LSTM+SC model. As well as, the model generates semantic words related to topics that are fed into the word embedding like Skip-Gram and FrameNet to build the domain-specific lexicon. The topically related sentences in each document are contextually grouped using Skip-Phrase. Each sentence in contextual group is given to Semantic Role Labelling (SRL). SRL indentify the essential predicate-argument structures with the semantic labels like verb (V) tag or ARGM-NEG or ARGM-PRP or ARGM-CAU or structures with the semantic labels like verb (V) tag or ARGM-NEG or ARGM-PRP or ARGM-CAU or ARGM-MNR or ARGM-MOD. The selected predicate-argument structures are aggregated into a linear layer to form a semantic embedding. Simultaneously, the predicate-argument embedding is segmented to sub words by BERT. The sub-words are transformed to word level through a convolutional layer to acquire the contextual word representation. Finally, semantic embedding and word representation are integrated to efficiently find the emotion of the given sentence. The experimental result proved that the proposed approach outperforms all the state-of-art approaches.

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Data Availability

The ISEAR, CrowdFlower, TEC and Tales, ECE and ECPE dataset utilized in this work for experimental evaluation purpose are publicly available datasets introduced by [10, 33, 43, 58, 62] and [74], respectively. The data generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors, thanks to all the anonymous reviewers of Multimedia Tools and Applications Journal for their constructive remarks and fruitful suggestions to improve the manuscript.

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  1. Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

    Vishnu Priya R.

  2. Department of Mathematics, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology (MANIT), Link Road No. 3, Bhopal, 462003, Madhya Pradesh, India

    Prashant Kumar Nag

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R., V.P., Nag, P.K. Text-based emotion recognition using contextual phrase embedding model. Multimed Tools Appl 82, 35329–35355 (2023). https://doi.org/10.1007/s11042-023-14524-9

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