This paper is published in Volume 1, Issue 3, 2018
Area
Computer Science
Author
Gitesh Mudgal
Co-authors
Rohini Temkar
Org/Univ
Vivekanand Education Society Institute Of Technology, chembur, Mumbai, Maharashtra, India
Pub. Date
02 July, 2018
Paper ID
V1I3-1144
Publisher
Keywords
Analysis, Sentiment Analysis, Sentiments on Social Media, Emerging subject mining, Big data.

Citationsacebook

IEEE
Gitesh Mudgal, Rohini Temkar. Analyzing and interpreting variations of public sentiments on social network, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJERnD.com.

APA
Gitesh Mudgal, Rohini Temkar (2018). Analyzing and interpreting variations of public sentiments on social network. International Journal of Advance Research, Ideas and Innovations in Technology, 1(3) www.IJERnD.com.

MLA
Gitesh Mudgal, Rohini Temkar. "Analyzing and interpreting variations of public sentiments on social network." International Journal of Advance Research, Ideas and Innovations in Technology 1.3 (2018). www.IJERnD.com.

Abstract

Social networks have become one of the most popular means of communication on the internet, as a result of which internet users have grown rapidly. Millions of messages are regularly available on websites that offer web services such as Facebook, Twitter, WhatsApp and LinkedIn. Millions of users share their personal opinions or views on various topics and discuss recent news on social websites, making it an important basis for tracking and analyzing the sentimental perception of the community. A social site is just as innovative as a small blog platform with more than one million unique weekly guests. On social sites each user placed a message with the name twitt or blog, which is visible to everyone. Such monitoring or analysis can provide important information for making decisions and evaluating opinions in different domains. In this work we went a step further to interpret the mood swings. We have established that the emerging topics within the periods of mood variation are strongly related to the real reasons for the variations. We propose a model based on latent Dirichlet allocation, LDA for foreground and background (FB-LDA) to distil the foreground themes and filter the age-old background themes. These foreground themes help to interpret mood swings in social networks. The feeling analysis, also known as Opinion Mining, plays a crucial role in determining the feelings in different web content. The analysis of opinions is very important to make decisions. Example, if you want to buy a new phone, a competent buyer of the web will always first assess the opinions to make a purchasing decision based on other experiences. The analysis of feelings extracts opinions, feelings and emotions from the text and analyzes them. This information is very useful for governments, companies and individuals. Although this content might be useful for analyzing most of the content generated by the user, it is difficult and time-consuming. Sentiment analysis is the automatic extraction of opinions, perspectives, and emotions from data sources via NLP.
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