Aspect-Based Sentiment Analysis Using a Hybrid Approach with Dependency Parsing

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 04 | 01 | Page :
    By

    Shravani B. Nikam,

  • Ishika B. Mulekar,

  • Diya A. Deshmukh,

  • Sanuja K. Khutale,

  • Neelima S. Ambekar,

  1. , Department of Artificial Intelligence and Data Science, MET Bhujbal Knowledge City, Nashik, Maharashtra,
  2. , Department of Artificial Intelligence and Data Science, MET Bhujbal Knowledge City, Nashik, Maharashtra, India
  3. , Department of Artificial Intelligence and Data Science, MET Bhujbal Knowledge City, Nashik, Maharashtra, India
  4. , Department of Artificial Intelligence and Data Science, MET Bhujbal Knowledge City, Nashik, Maharashtra, India
  5. , Department of Artificial Intelligence and Data Science, MET Bhujbal Knowledge City, Nashik, Maharashtra, India

Abstract

The exponential growth of textual data in today’s digital era presents organizations with the challenge of efficiently analyzing consumer-generated content across diverse platforms. However, many existing models fail to process syntactic and semantic information, leading to incomplete sentiment analysis. To address this limitation, we propose a unified system combining dependency parsing and semantic role labelling to comprehensively understand textual data. This system introduces a hybrid feature vector that integrates features derived from overall reviews with those specific to identified aspects and opinions, enhancing sentiment classification accuracy. Additionally, Bidirectional Encoder Representations from Transformers categorize user reviews into three sentiment polarities: positive, negative, and neutral. By employing a hybrid feature extraction strategy,the proposed system can effectively process complex textual inputs, offering a robust and efficient solution for sentiment analysis.

Keywords: Sentiment analysis, dependency parsing, semantic role labelling, hybrid feature extraction, Bidirectional Encoder Representations from Transformers (BERT), text classification, consumer behavior

How to cite this article:
Shravani B. Nikam, Ishika B. Mulekar, Diya A. Deshmukh, Sanuja K. Khutale, Neelima S. Ambekar. Aspect-Based Sentiment Analysis Using a Hybrid Approach with Dependency Parsing. International Journal of Algorithms Design and Analysis Review. 2025; 04(01):-.
How to cite this URL:
Shravani B. Nikam, Ishika B. Mulekar, Diya A. Deshmukh, Sanuja K. Khutale, Neelima S. Ambekar. Aspect-Based Sentiment Analysis Using a Hybrid Approach with Dependency Parsing. International Journal of Algorithms Design and Analysis Review. 2025; 04(01):-. Available from: https://journals.stmjournals.com/ijadar/article=2025/view=232459


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Ahead of Print Subscription Review Article
Volume 04
01
Received 16/06/2025
Accepted 12/09/2025
Published 18/11/2025
Publication Time 155 Days
116

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