TASCI: transformers for aspect-based sentiment analysis with contextual intent integration

TASCI: transformers for aspect-based sentiment analysis with contextual intent integration

In this article, we present a novel Transformer-Based Aspect-Level Sentiment Classification with Intent (TASCI) model, designed to enhance sentiment analysis by integrating aspect-level sentiment classification with intent analysis. Traditional sentiment analysis methods often overlook the nuanced r...

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Bibliographic Details
Main Authors: Hassan Nazeer Chaudhry, Farzana Kulsoom, Zahid Ullah Khan, Muhammad Aman, Sajid Ullah Khan, Abdullah Albanyan
Format: Article
Language:English
Published: PeerJ Inc. 2025-05-01
Series:PeerJ Computer Science
Subjects:
Sentimental analysis
Transformers
Natural language processing
Aspect level sentimental classification
Online Access:https://peerj.com/articles/cs-2760.pdf
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Summary:In this article, we present a novel Transformer-Based Aspect-Level Sentiment Classification with Intent (TASCI) model, designed to enhance sentiment analysis by integrating aspect-level sentiment classification with intent analysis. Traditional sentiment analysis methods often overlook the nuanced relationship between the intent behind a statement and the sentiment expressed toward specific aspects of an entity. TASCI addresses this gap by first extracting aspects using a self-attention mechanism and then employing a Transformer-based model to infer the speaker’s intent from preceding sentences. This dual approach allows TASCI to contextualize sentiment analysis, providing a more accurate reflection of user opinions. We validate TASCI’s performance on three benchmark datasets: Restaurant, Laptop, and Twitter, achieving state-of-the-art results with an accuracy of 89.10% and a macro-F1 score of 83.38% on the Restaurant dataset, 84.81% accuracy and 78.63% macro-F1 score on the Laptop dataset, and 79.08% accuracy and 77.27% macro-F1 score on the Twitter dataset. These results demonstrate that incorporating intent analysis significantly enhances the model’s ability to capture complex sentiment expressions across different domains, thereby setting a new standard for aspect-level sentiment classification.
ISSN:2376-5992

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