Aspect-Based Sentiment Analysis Advancements and Applications in Code-Mixed Text and Gujarati Language Processing
Aspect-Based Sentiment Analysis Advancements and Applications in Code-Mixed Text and Gujarati Language Processing |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-9 |
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| Year of Publication : 2025 | ||
| Author : Shyam Viththalani, Krunalkumar Patel | ||
| DOI : 10.14445/22315381/IJETT-V73I9P130 | ||
How to Cite?
Shyam Viththalani, Krunalkumar Patel,"Aspect-Based Sentiment Analysis Advancements and Applications in Code-Mixed Text and Gujarati Language Processing", International Journal of Engineering Trends and Technology, vol. 73, no. 9, pp.356-368, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I9P130
Abstract
Aspect-Based Sentiment Analysis (ABSA), on the other hand, gives more granular results regarding the sentiments being expressed specifically for the aspects, but this has only been limited work done for code-mixed languages or regional languages like Gujarati. This paper presents a novel approach to deal with the limitations, such as syntax mixing of code-mixed text, considering the morphological features of Gujarati. By using specific preprocessing, aspect extraction methods and classifiers for multilingual and low-resource scenarios, the proposed approach outperforms the basic solutions. Its usefulness is further demonstrated with real-world datasets, thus its applicability to social media surveillance and regional sentiment analysis has great potential for incorporating culturally sensitive natural language processing. Aspect-Based Sentiment Analysis (ABSA) is a critical area within Natural Language Processing (NLP) that enables detailed sentiment interpretation by associating opinions with specific aspects mentioned in text. While ABSA has seen substantial advancements in high-resource languages, its application to code-mixed texts and low-resource languages such as Gujarati remains relatively limited. This paper explores recent progress in ABSA, with a particular focus on two linguistically challenging domains: Hindi-English code-mixed texts and monolingual Gujarati content. This paper highlights key obstacles, including language mixing, orthographic inconsistencies, and the scarcity of annotated datasets. To tackle these challenges, the study investigates hybrid strategies that combine deep learning models (e.g., LSTM, BERT) with sentiment lexicons, along with emerging techniques such as contrastive learning and multilingual transformer architectures. Additionally, a newly developed Gujarati sentiment corpus is presented and assessed using various machine learning and lexicon-based methods for aspect-level sentiment classification. The experimental results underscore the importance of customized feature extraction, language-aware pre-processing, and ensemble approaches in enhancing ABSA performance for multilingual and low-resource settings. The study aims to broaden the scope of sentiment analysis by offering methodologies and resources tailored to underrepresented languages and code-mixed communication.
Keywords
Aspect-Based Sentiment Analysis, Sentiment Analysis, Code-Mixed Language, Gujarati Language, Multilingual NLP, Low-Resource Languages, Sentiment Classification, Aspect Extraction, Social Media Analysis.
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