Sentiment Analysis of Multilingual Social Media Posts Using Deep Learning Models: A Case Study on Marathi-English Code-Switching
Varsharani T. Dond1 ; Suhasini R. Bilure2
1 Assistant Professor, PVG’s College of Science and Commerce, Pune, Maharashtra, India.
2 Research Scholar, School of Languages and Literature, Punyshlok Ahilyadevi Holkar Solapur University, Solapur.
Email id: varshadond14@gmail.com 1, biluresuhasini@gmail.com2
1.Abstract: With the widespread adoption of social mass medium platforms, the bulk of user-generated content has expanded dramatically. This digital discourse, especially in multilingual regions such as India, oftentimes features codification-switch—the practice of understudy between two or more words within an exclusive sentence or conversation. This stage unique challenge in sentiment analysis, particularly when analyzing low-imagination language pairs like Marathi and English. This study proposes a comprehensive, intercrossed deep learning glide path to in effect sort sentiments from such computer code-interchange data point. A curated dataset of 15, 000 Marathi-English social sensitive posts is gather and manually annotated for sentiment mutual opposition. Versatile preprocessing stair including Romanized school text normalization and crossbreed tokenization are employed to develop the dataset. A hybrid model integrating Convolutional Neural Networks (CNN), Long Short-Term Memory meshwork (LSTM), and Grey Wolf Optimization (GWO) is propose and benchmarked against traditional modeling such as Naive Bayes, Support Vector Machine (SVM), BiLSTM, mBERT, and XLM-RoBERTa. The results certify superior truth, recall, and F1-account for the purpose CNN-LSTM-GWO poser. Additionally, a detailed cause study examines sentiment patterns in real-world cultural and political effect. This subject field contributes to the progress of sentiment analysis in computer code-switched, low-resourceful voice communication, offering insight and practical method acting applicable to multilingual NLP and social spiritualist monitoring systems.
Keywords: Sentiment Analysis, Code-Switching, Marathi-English, Deep Learning, Transformer Models, CNN, LSTM, GWO, Social Media NLP, mBERT, XLM-RoBERTa, Romanized Text, Hybrid Architecture, Low-Resource Languages
