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MuCon: Multi-channel convolution for targeted sentiment classification

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Abstract

Targeted Sentiment Analysis goes beyond general sentiment classification tasks by aiming to identify the sentiment of a specific target aspect within a given text. Previous studies have predominantly utilized recurrent neural networks (RNN) or their variants to predict target-specific sentiment polarity. However, the sequential processing nature of RNN restricts parallelization and fails to leverage the potential of modern multicore architectures. Additionally, these models often overlook the inherent linguistic perspective embedded in the text. This paper proposes a novel approach called MuCon (Multi-channel Convolution), which employs a simple yet effective convolutional neural network (CNN) model. MuCon incorporates multiple channels dedicated to linguistic and statistical features to determine aspect-specific sentiment polarity accurately. By incorporating linguistic knowledge into a statistical model, MuCon performs better and achieves comparable results to sophisticated state-of-the-art methods.

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Notes

  1. https://www.yelp.com/dataset/challenge

  2. https://cseweb.ucsd.edu/~jmcauley/datasets/amazon_v2/

  3. We have used Spacy for dependency relation extraction https://spacy.io/

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Funding

This work was partially financially supported by TEQIP-III, REC Ambedkar Nagar, UP, India.

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  1. Rajkiya Engineering College, Ambedkar Nagar, Uttar Pradesh, 224122, India

    Sharad Verma

  2. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    Sharad Verma, Ashish Kumar & Aditi Sharan

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  1. Sharad Verma
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Correspondence to Ashish Kumar.

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Verma, S., Kumar, A. & Sharan, A. MuCon: Multi-channel convolution for targeted sentiment classification. Multimed Tools Appl 83, 28615–28633 (2024). https://doi.org/10.1007/s11042-023-16586-1

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