학술논문

A Study on Sentiment Analysis Using Various ML/NLP Models on Historical Data of Indian Leaders
Document Type
Conference
Source
2024 3rd International Conference for Innovation in Technology (INOCON) Innovation in Technology (INOCON), 2024 3rd International Conference for. :1-8 Mar, 2024
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Sentiment analysis
Analytical models
Technological innovation
Schedules
Video on demand
Data models
Web sites
Multilingual Sentiment Analysis
Marathi
Natural Language Processing
Text Summarization
Lexicon-based Approaches
Language
Abstract
Among the highly significant duties for any language most effective is the sentiment analysis, which is also a key area of NLP, that recently made impressive strides. There are several models and the datasets available for those tasks in popular and commonly used languages like English, Russian, and Spanish. While sentiment analysis research is performed extensively, however it is lagging behind for the regional languages having few resources such as Hindi, Marathi. Marathi is one of the languages that included in the Indian Constitution’s 8th schedule and is the third most widely spoken language in the country and primarily spoken in the Deccan region, which encompasses Maharashtra and Goa. There isn’t sufficient study on sentiment analysis methods based on Marathi text due to lack of available resources, information. Therefore, this project proposes the use of different ML/NLP models for the analysis of Marathi data from the comments below YouTube content, tweets or Instagram posts. We aim at the short and precise analysis and summary of the related data using our dataset (Dates, names, root words) and lexicons to locate exact information.