Analyzing Social Emotions in Social Network Using Graph Based Co-Ranking Algorithm

Analyzing Social Emotions in Social Network Using Graph Based Co-Ranking Algorithm

Kani Priya (Hindustan University, Chennai, India), Krishnaveni R. (Hindustan University, Chennai, India), Krishnamurthy M. (KCG College of Technology, Chennai, India) and Bairavel S. (KCG College of Technology, Chennai, India)
Copyright: © 2020 |Pages: 11
DOI: 10.4018/IJTHI.2020040103

Abstract

Twitter has become exceedingly popular, with hundreds of millions of tweets being posted every day on a wide variety of topics. This has helped make real-time search applications possible with leading search engines routinely displaying relevant tweets in response to user queries. Recent research has shown that a considerable fraction of these tweets are about “events,” and the detection of novel events in the tweet-stream has attracted a lot of research interest. However, very little research has focused on properly displaying this real-time information about events. For instance, the leading search engines simply display all tweets matching the queries in reverse chronological order. Online content exhibits rich temporal dynamics, and diverse real-time user generated content further intensifies this process. However, temporal patterns by which online content grows and fades over time, and by which different pieces of content compete for attention remain largely unexplored. This article describes tracking and analyzing public sentiment on social networks and finding the possible reasons causing these variations. It is important to find the decision from public views and opinion in different domain. They can be used to discover special topics or aspects in one text collection in comparison with another background text collection. The implemented method attains the 95% accuracy while predict the sentiments from the social websites and the 96.3% of the opinion rate with minimum time.
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In this section analyze the various authors opinion involved in the sentiment examination process in social sites. Shulong Tan et al. (2014) proposes a Latent Dirichlet Allocation (LDA) based model. To distill foreground topics and filter out longstanding background topics, Foreground and Background LDA (FB-LDA) was used. To rank them with respect to their “popularity” within the variation period, Reason Candidate and Background LDA (RCB-LDA) was used. Alexandre Trilla et al. (2013) proposes the work focuses on categorization of a plain input text to inform a TTS system about the most appropriate sentiment (+ve, -ve, neutral) to automatically synthesize expressive speech at the sentence level. Alena Neviarouskaya et al. (2011) proposes the Latent Semantic Analysis (LSA) which assumes that words that are close in meaning will occur in similar pieces of text. Analyzing relationships between a set of documents and the terms contain in it. Parisa Lak et al. (2014) proposes the online product/service reviews serve as sources of product/service-related information star ratings provide a quick indication of tone of a review. In some cases, it is not available or detailed enough. Sentiment analysis automatically detect the polarity of text, i.e. more refined analysis.

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