Open AccessSentiment Polarity Identification of Social Media content using Artificial Neural Networks
Author(s) -
K. Victor Rajan,
Brittney Jackson
Publication year - 2022
Publication title -
global journal of computer science and technology
Language(s) - English
Resource type - Journals
ISSN - 0975-4172
DOI - 10.34257/gjcstdvol22is1pg1
Subject(s) - sentiment analysis , computer science , social media , point (geometry) , identification (biology) , polarity (international relations) , government (linguistics) , task (project management) , service (business) , microblogging , world wide web , data science , artificial intelligence , marketing , business , linguistics , philosophy , botany , geometry , mathematics , genetics , management , cell , economics , biology
Sentiment of people about consumer goods and government policies for decision making is normally collected through feedback forms, surveys etc. The social network sites and micro blogging sites are considered a very good source of information nowadays because people share and discuss their opinions about a certain topic freely. With the increased use of technology and social media, people proactively express their opinion through social media sites like Twitter, Facebook, Instagram etc. A social media sentiment analysis can help companies to understand how people feel about their products. On the other hand, extracting the sentiment from social media text is a challenging task due to the complexity of natural language processing of social media language. Often these messages reflect the emotion, opinion and sentiment of the public through a mix of text, image, emoticons etc. These statements are often called electronic Word of Mouth (eWOM) and are much prevalent in business and service industry to enable customers to share their point of view.