Papers by RAMNATH M . GAIKWAD
IJRASET, 2023
Sentiment analysis on numerous Regional languages is performed, and classification algorithms bas... more Sentiment analysis on numerous Regional languages is performed, and classification algorithms based on Lexicon, Dictionary, and Machine Learning are employed. Because of the widespread usage of social media platforms, people are rapidly turning to the internet to find and discuss information, thoughts, opinions, feelings, perspectives, facts, and suggestions, resulting in a plethora of user-generated emotion enormous amounts of text data available for analysis. A large number of individuals in India express themselves in multiple languages, resulting in a massive amount of Natural Language Processing text data for (NLP) researchers. Sentiment Analysis (SA) of code-mixed text provides valuable information in politics, education, services marketing, business, health, sports, and other sectors. Work on Indian Language Sentiment Analysis Textual Data, particularly in Hindi, has gained steam in the previous decade in comparison to code-mixed Indian language text. However, due to a lack of language and vocabulary (linguistic and lexical) tools and annotated resources, the process of Sentiment Analysis of Regional Languages becomes very difficult. The goal of this research was to present a complete summary of the Sentiment Analysis of Regional languages, with a focus on code-mixed Regional languages.
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2024 IEEE International Conference on Contemporary Computing and Communications (InC4), 2024
In the era of digital communication, understanding public sentiment is crucial. However, sentimen... more In the era of digital communication, understanding public sentiment is crucial. However, sentiment analysis tools for less common languages like Marathi are limited. This paper introduces a machine learning and deep learning approach to Marathi sentiment analysis using Senticnet. Through the utilization of Senticnet and various machine learning techniques, we collected and pre-processed data, adapt Senticnet for Marathi, and design language-specific sentiment analysis models. Leveraging techniques such as tokenization, text cleaning, and feature extraction, we effectively classified Marathi text into positive, negative, and neutral sentiments. Our library's performance is evaluated against existing tools, show casing its accuracy and sensitivity to Marathi sentiment nuances. This work not only enhances Marathi sentiment analysis but also offers insights into adapting resources for non-english languages. By sharing our methodology and library, we encourage further research in regional languages, promoting sentiment analysis in diverse linguistic landscapes. The significant this research focuses on the creation of robust Marathi sentiment analysis tool, facilitating deeper understanding and analysis of sentiments in underrepresented languages, and serving as a catalyst for future advancements in sentiment analysis across linguistic boundaries.
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Papers by RAMNATH M . GAIKWAD
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