Open AccessSentiment Analysis-Enhancements and Applications
Author(s) -
Aayush Gupta,
Anant Gandhi,
Saarthak Agarwal,
Shamin Chokshi,
Kailash Karthik Saravanakumar
Publication year - 2021
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a1409.1110421
Subject(s) - sentiment analysis , computer science , parsing , natural language processing , perspective (graphical) , artificial intelligence , domain (mathematical analysis) , vocabulary , point (geometry) , subject (documents) , product (mathematics) , semantic analysis (machine learning) , semantics (computer science) , service (business) , data science , linguistics , world wide web , mathematical analysis , philosophy , geometry , mathematics , economy , economics , programming language
The concept of Natural Language Processing that deals with problems of identifying the sentiment from the voice or text of a speaker or writer and then use that analysis further for making predictions, market survey, customer service, product satisfaction, precision targeting etc. is called Sentiment analysis. From one viewpoint, it is an abstract evaluation of something dependent on close to home observational experience. It Is mostly established in target realities and incompletely governed by feelings. Then again, a sentiment can be deciphered as a kind of measurement in the information in regards to a specific subject. It is a lot of markers that mix present a point of view, i.e., perspective for the specific issue. So as to enhance the accuracy of sentiment analysis/classification, it is imperative to appropriately recognize the semantic connections between the various words and phrases that are describing the subject or aspect. This can be done by applying semantic analysis with a syntactic parser and supposition vocabulary. This research will discuss different sets of approaches for application or domain specific problems and then compare them to obtain the best possible approaches to the problem of sentiment analysis.