Sentiment Analysis of Twitter Posts in English, Kannada and Hindi languages

Exploring Sentiment Analysis in Indian Regional Languages: Methods, Challenges, and Future Directions

“…These studies have addressed challenges such as informal language, slang, and emoticon usage typical of social media text, with BERT models demonstrating superior performance due to their ability to capture contextual information effectively. The exploration of these models across various languages and contexts emphasizes the dynamic nature of sentiment analysis research and its potential for future advancements [ 1,3,4,5,7,9,10,11,13,14,15,20,22,23,24,30].For languages with limited computational resources, such as Marathi and Urdu, lexicon-based approaches have been proposed as effective methods for sentiment analysis. Researchers have developed lexicons that include lists of positive and negative words, assigning polarity values to facilitate the classification of sentences into sentiments.…”

“…Future work is suggested to focus on expanding the classification capabilities to include figurative language, enriching datasets with more diverse samples, exploring additional algorithms for enhanced accuracy, and further developing sentiment analysis models to accommodate low-resource languages. These directions underscore the evolving nature of sentiment analysis research and its critical role in understanding and leveraging user-generated content in multilingual societies [1,2,3,4,5,7,9,10,11,13,14,15,20,22,23,24,30,31,32].…”

“…The challenges highlighted include English-based phonetic typing and word-level mixing, underscoring the need for models capable of effectively processing code-mixed data. These studies contribute to understanding sentiment trends among speakers of Dravidian languages on social media platforms [5,7,13,14,15].Research in Urdu sentiment analysis underscores the complexity of the language's morphological structure and the absence of standard datasets or corpora. Efforts to create benchmark datasets and evaluate the performance of machine learning and deep learning techniques, including CNN-1D and LSTM, are noted.…”