Marathi NLP: Sentiment Analysis, Resources, and Future Prospects

Discovering Marathi: The Language's Journey in Natural Language Processing

Marathi is a vibrant language, rich in culture and history, and it's also the third most spoken language in India, with around 83 million speakers worldwide. In recent years, researchers have been investing considerable effort in advancing Marathi language processing, particularly in natural language processing (NLP) and related technologies.

Sentiment Analysis in Marathi

Marathi sentiment analysis has gained traction with the rise of digital communication. Studies have employed various techniques, including bilingual dictionaries, HSWN (Hierarchical Sentence Word Net), and genetic algorithms, to identify and classify sentiments in Marathi content.

Recent research has focused on multimodal sentiment analysis, incorporating both textual and visual cues, to improve sentiment classification, particularly on social media platforms.

Resources, Tools, and Techniques

Marathi NLP research faces several challenges, such as limited resources (corpora, dictionaries, and tools) and the complexity of the language, which includes multiple dialects and morphological intricacies.

Despite these challenges, researchers have been working on developing state-of-the-art techniques for various NLP tasks, including part-of-speech tagging, named entity recognition, and morphological analysis.

Speech Recognition

Speech recognition has been another area of interest for Marathi language researchers, given the potential to bridge the digital divide between the technically illiterate and the mainstream population.

Future Prospects

Although progress has been significant, there is still an urgent need for more resources, tools, and techniques to unlock Marathi's full potential. Advanced NLP techniques, including deep learning and multimodal approaches, will likely play a crucial role in further enhancing Marathi language processing.

The Marathi language community, with its rich heritage and promising future, embraces the challenges of NLP research and strives to contribute to the global dialogue on language processing.