Aug 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 775-799· 0 citations
TL;DR
Overall, this work demonstrates that incorporating explicit linguistic information, including language identity, sentiment polarity, and intensifier information, improves sentiment classification of Gujarati–English code-mixed text.
Abstract
Code-mixed text, in which words from multiple languages are used within the same sentence, is common on social media platforms and poses significant challenges for conventional natural language processing techniques. This study focuses on five-class sentiment classification of of Gujarati–English code-mixed text. The dataset consists of 5 sentiment classes ranging from extremely negative to extremely positive distributed over 44,672 sentences. A linguistically informed framework is proposed that incorporates word-level annotations, including language identity, sentiment polarity, and intensifier presence, generated using a multi-task fine-tuned DistilBERT tagger. These annotations are aggregated into sentence-level handcrafted features and combined with conventional text representations, namely Bag of Words (BoW), Term Frequency–Inverse Document Frequency (TF-IDF), Word2Vec, and FastText. The proposed framework is evaluated using a late fusion approach based on out-of-fold (OOF) stacking and is compared with early fusion, where features are directly concatenated, and with embedding-only baselines. The statistical significance of performance differences is assessed using McNemar's test. Seven machine learning classifiers—Logistic Regression (LR), Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boost(XGB)—are evaluated. The hyperparameters of all classifiers are optimized using GridSearchCV and then kept fixed throughout the experiments to ensure a fair comparison. Class imbalance is addressed using cost-sensitive learning through class-weight adjustment. The experimental results show that early fusion of linguistic and text features consistently outperforms the embedding-only and late fusion approaches for most classifier–representation combinations. The best-performing model is XGB with FastText under the early fusion framework, achieving an accuracy of 0.78 and a macro F1-score of 0.74 on the unseen test set. Overall, this work demonstrates that incorporating explicit linguistic information, including language identity, sentiment polarity, and intensifier information, improves sentiment classification of Gujarati–English code-mixed text. In addition, it provides a comprehensive and statistically validated comparison of feature integration strategies for sentiment analysis in low-resource, code-mixed language settings.
A Hybrid VADER–IndoBERT framework designed to improve sentiment classification robustness on complex Indonesian texts is introduced, demonstrating the superiority of Transformer-based architectures in capturing long-range dependencies and handling ambiguous sentiment cues.
Margareta Valencia Suci Handayani, R. S. Basuki, Muljono et al.· Jurnal RESTI (Rekayasa Siste...· 0 citations
The exponential rise of social media has led to the generation of a large amount of informal text. Especially, code-mixed languages have gained substantial popularity among social media users. In India, the code-mixed language Kannada-English is widely used in social media platforms. This informal and non-standard language form brings forward significant challenges to natural language processing (NLP) tasks like sentiment analysis. This paper presents a detailed analysis of sentiment analysis in kannada-english code-mixed social media text. A manually annotated dataset is created and classified into positive, negative, and neutral sentiment labels. Various kinds of machine learning, deep learning, and transformer-based models are evaluated. The results suggest that the transformer-based models trained on Indian language corpora outperform others. Furthermore, this paper provides a comprehensive set of observations regarding sentiment analysis of Indian language code-mixed social media text.
D. M, Dr. Sasikala P, D. N et al.· International journal of res...· 0 citations
Considering the growth of multilingual user made content within social-media platforms, there is an urgent need for developing scalable, language-agnostic approaches for their analysis. Within this paper, we analyze mBERT's performance in sentiment classification in a binary setting as well as the possibility of performing transfer learning between languages. Specifically, the fine-tuned model is applied for sentiment analysis of tweets from the preprocessed TweetEval dataset, obtaining 79.2% of accuracy and 74.7% of the F1 score. It is shown that cross-language transfer learning without any preliminary training on multilingual sentiment datasets provides quite satisfactory performance. However, a more complex approach can be used, which consists of applying filtering of negative sentiments, categorization of subcategories through a sentence transformer with zero-shot settings, and grouping the resulting data in several major categories to obtain severity scores according to frequency thresholds. The application of the sentiment classification with transformers in combination with issue prioritization makes it possible to develop an end-to-end approach to structuring multilingual social media content.
S. Nahar, P. P. Agnihotri· International Journal of Sci...· 0 citations
Part Of Speech (POS) tagging is a fundamental task in Natural Language Processing (NLP) that assigns grammatical labels to words in a sentence. Code mixed text, which entails switching between two or more languages within a single conversation or a sentence, presents challenges for POS tagging. This investigation entailed a comprehensive study of deep learning approaches for cross linguistic POS tagging, focused on English Malayalam code mixed data prevalent on social media platforms. The study was carried out on linguistically complex and varied English Malayalam code mixed text from social media platforms with informal spellings, language switching, transliteration, slang, and unclear grammatical boundaries, reflecting the characteristics of informal online communication. We provide the first structured analysis of micro macro divergence and rare class behavior in English Malayalam code mixed POS tagging.
We evaluated 14 state of the art model configurations that span traditional sequence labeling approaches and multilingual transformer architectures. Models were compared using standard performance metrics prevalent in the domain of data science, supplemented by normalized confusion matrices, error prone tag identification and micro-macro F1 gap analysis.
Our results showed that CRF (No Lang) emerged as the most balanced model overall on macro F1 (all classes) of 0.8170, while (BiLSTM + CRF) achieved the highest macro F1 (seen classes) of 0.8831, precision of 0.9167, and recall of 0.875, though this reflects strong performance concentrated on frequent tag classes rather than balanced coverage across the full tag set. Notably, the pretrained multilingual transformers (mBERT, MuRIL), despite prior exposure to Malayalam during pretraining, were outperformed on several key metrics by CRF and BiLSTM models trained directly on the code mixed dataset.
This finding was contrary to our expectation that existing multilingual knowledge would translate into a clear advantage on this task and merits further investigation.
Unknown authors· Frontiers in Big Data· 0 citations
The research proves that RoBERTa is a very powerful and reliable model for the current sentiment classification problems and can be a major step towards developing intelligent opinion mining and automated text analysis systems.
Vemula Vandana, K. Ushamahalaxmi, Dr. L Jagadeesh Naik· International Journal of Dru...· 0 citations
The widespread adoption of social media platforms has increased the availability of code-mixed textual data, particularly for low-resource languages such as Marathi–English. But sentiment analysis has considerable obstacles stemming from multilingual diversity, transliteration complexity, and the scarcity of high-quality annotated datasets. This work presents `KonkaniFood 2.0`, a 5,195 YouTube comments dataset of Konkani cuisine, collected through web scraping and carefully annotated for positive, negative and neutral sentiment (3-Class), with a Fleiss's kappa score of 0.967. Sentiment categorisation was carried out using Advanced Transformer-Based Language Models: mBERT, MuRIL,and IndicBERT. MuRIL model showed a better understanding of the semantic nuances of code-mixed text with 98% accuracy. Moreover, explainable AI (XAI) approaches were employed to interpret the model predictions by highlighting sentiment-carrying phrases, hence boosting transparency and trustworthiness. This work offers a valuable resource in Marathi-English (Mr-En) resource-scarce, code-mixed multilingual sentiment analysis.
A. Phadke, S. Kohle, Praveen Gupta· 2026 4th International Confe...· 0 citations
