A Multichannel Convolutional Neural Network for Multilabel Sentiment Classification Using Abilify Oral User Reviews

Authors

  • Tina Esther Trueman Department of Computer Science, University of the People, Pasadena, United States
  • Ashok Kumar Jayaramn Department of Information Science and Technology, Anna University, Chennai - 600025, India
  • Jasmine S Department of Information Science and Technology, Anna University, Chennai - 600025, India
  • Gayathri Ananthakrishnan Department of Information Technology, Vellore Institute of Technology, Vellore, India
  • Narayanasamy P Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, India

DOI:

https://doi.org/10.31449/inf.v47i1.3510

Abstract

Nowadays, patients and caregivers have become very active in social media. They are sharing a lot of information about their medication and drugs in terms of posts or comments. Therefore, sentiment analysis plays an active role to compute those posts or comments. However, each post is associated with multilabel such as ease of use, effectiveness, and satisfaction. To solve this kind of problem, we propose a multichannel convolution neural network for multilabel sentiment classification using Abilify oral user comments. The multichannel represents the multiple versions of the standard model with different strides. Specifically, we use the pre-trained model to generate word vectors. The proposed model is evaluated with multilabel metrics. The results indicate that the proposed multichannel convolutional network model outperforms the traditional machine learning algorithms.

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Published

2023-05-12

How to Cite

Trueman, T. E., Jayaramn, A. K., S, J., Ananthakrishnan, G., & P, N. (2023). A Multichannel Convolutional Neural Network for Multilabel Sentiment Classification Using Abilify Oral User Reviews. Informatica, 47(1). https://doi.org/10.31449/inf.v47i1.3510

Issue

Vol. 47 No. 1 (2023)

Section

Regular papers

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