Predicting collective sentiment dynamics from time-series social media

Nguyen, Loc H.; Wu, Pang; Chan, William; Peng, Wei; Zhang, Ying

doi:10.1145/2346676.2346682

Cited by 80 publications

(45 citation statements)

References 13 publications

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“…In [8] authors examined the predictive analysis on webbased networking media time-series enables the stakeholders to use this immediate, accessible and vast reachable communication channel to respond and proact against the public opinion. Specifically, understanding and calculating the sentiment change of the public opinions will enable business and government organizations to respond against negative sentiment and design strategies, for example, dispelling gossips and post balanced messages to return the public opinion.…”

Section: Literature Surveymentioning

confidence: 99%

A Comparative Study Ofsentimental Analysis in Various Techniques

Indhu¹

2017

ijarcs

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Social media is one of the significant places to show user's feelings. In sentiment analysis the information is separated from the notion, assessment and feelings of individuals with respect to entities, events, and their characteristics. Sentiment analysis is also known as Opinion Topic Modeling (OTM) approach. OTM is used to examine and group the user created information likecomments, articles and so on. Thisinformation's are taken from the social networking sites like facebook, twitter and so on. Twitter has given a huge space for analysing movie reviews, consumer brands, election events, stock market exchange, and so on. This survey paper discusses several methods used for sentiment analysis.

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Section: Literature Surveymentioning

confidence: 99%

A Comparative Study Ofsentimental Analysis in Various Techniques

Indhu¹

2017

ijarcs

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“…This correlation sentiment words procedure is best prediction procedure compare to above all procedures [2] [3] [5].…”

Section: Related Workmentioning

confidence: 99%

Design of Sentiment Analysis System using Polarity Classification Technique

Kodipaka¹,

Polepaka²,

Rafeeq³

2015

IJCA

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Twitter is a medium that we can use for communication. All posted tweets we can store in one location and create archive. Archive contains new and old tweets. Now we can start the analyzation on archive tweets that's we can design effective sentiment analysis system. This paper main aim is to determine parts of speech opinion words using polarity classification technique and support vector machine learning algorithm. Surveys of methods are used in various levels of sentiment analysis. It does analyze the tweets information in limited levels of content only. Now in this paper we design new sentiment analysis tool using polarity classification technique. Polarity classification techniques discover top 20-emoticons, learning different classes of words and other features information. These techniques perform in depth tweets analysis. It does provide better analysis results compare to previous methods.

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“…Due to the widespread diffusion of sentiment analysis, different studies have been provided in the literature with the aim to develop approaches for sentiment analysis processing in business domain (Fan et al, 2011;Wang et al, 2013). However the existing literature in this field appears fragmented: in some studies only a description of the sentiment extraction phase is provided (Rastogi et al, 2014;Duwairi et al, 2014;Khan et al, 2014;Bagwan et al, 2013;De Clercq et al, 2014) while other studies only consider sentiment changes detection (Bifet at al., 2011;Daas & Puts, 2014;Nguyen et al, 2012) and/or sentiment prediction (Park, 2004;Madrigal, 2001;Zhang et al, 2011a;Bollen et al, 2011;Gilbert & Karahalios, 2010). In order to overcome this fragmentation, in this paper an integrated methodology for approaching sentiment analysis in business domain is provided.…”

Section: Introductionmentioning

confidence: 99%

“…Two different steps of the sentiment changes detection have been considered for the analysis: sentiment determination and data selection/analysis. Also in the study provided by Nguyen et al (2012) a strategy to predict collective sentiment dynamics is provided. The collective sentiment changes are modeled by using a machine learning model without an analysis of individual tweets and their corresponding network structures.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Methodology for Approaching Sentiment Analysis in Business Domain

Ferri¹,

D'Andrea²,

Grifoni³

2017

IBR

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The paper provides a methodology for approaching sentiment analysis in the business domain. It involves the different phases that compose the sentiment analysis processing (sentiment extraction, sentiment changes detection and sentiment prediction). Each phase involves different steps and some examples of approaches/methods/models to perform them. An example of an application scenario for the methodology is defined in the paper.

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Predicting collective sentiment dynamics from time-series social media

Cited by 80 publications

References 13 publications

A Comparative Study Ofsentimental Analysis in Various Techniques

A Comparative Study Ofsentimental Analysis in Various Techniques

Design of Sentiment Analysis System using Polarity Classification Technique

An Integrated Methodology for Approaching Sentiment Analysis in Business Domain

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