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Explaining Sentiment Spikes in Twitter

Published: 24 October 2016 Publication History

Abstract

Tracking public opinion in social media provides important information to enterprises or governments during a decision making process. In addition, identifying and extracting the causes of sentiment spikes allows interested parties to redesign and adjust strategies with the aim to attract more positive sentiments. In this paper, we focus on the problem of tracking sentiment towards different entities, detecting sentiment spikes and on the problem of extracting and ranking the causes of a sentiment spike. Our approach combines LDA topic model with Relative Entropy. The former is used for extracting the topics discussed in the time window before the sentiment spike. The latter allows to rank the detected topics based on their contribution to the sentiment spike.

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  • (2024)Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media DataIEEE Access10.1109/ACCESS.2024.337158512(33504-33523)Online publication date: 2024
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  • (2023)Sentiment Reason Mining Framework for Analyzing Twitter Discourse on Critical Issues in US Healthcare Industry2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)10.1109/SCSE59836.2023.10215010(1-8)Online publication date: 29-Jun-2023
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cover image ACM Conferences
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
October 2016
2566 pages
ISBN:9781450340731
DOI:10.1145/2983323
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 24 October 2016

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Author Tags

  1. sentiment spikes
  2. tracking sentiment
  3. twitter

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CIKM'16
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CIKM'16: ACM Conference on Information and Knowledge Management
October 24 - 28, 2016
Indiana, Indianapolis, USA

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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  • (2024)Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media DataIEEE Access10.1109/ACCESS.2024.337158512(33504-33523)Online publication date: 2024
  • (2024)Can Twitter data with positive or negative content affect individual emotions related to travel & tourism decisions? A study pertaining to the COVID-19 pandemicCurrent Issues in Tourism10.1080/13683500.2023.2300044(1-15)Online publication date: Jan-2024
  • (2023)Sentiment Reason Mining Framework for Analyzing Twitter Discourse on Critical Issues in US Healthcare Industry2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)10.1109/SCSE59836.2023.10215010(1-8)Online publication date: 29-Jun-2023
  • (2021)Monitoring People’s Emotions and Symptoms from Arabic Tweets during the COVID-19 PandemicInformation10.3390/info1202008612:2(86)Online publication date: 19-Feb-2021
  • (2021)Perceiving Social-Emotional Volatility and Triggered Causes of COVID-19International Journal of Environmental Research and Public Health10.3390/ijerph1809459118:9(4591)Online publication date: 26-Apr-2021
  • (2021)Proposed Methodology for Sentiment Analysis of Social Media Data Focusing on the Sentiment Analysis in Political Domain2021 International Conference on Computing Sciences (ICCS)10.1109/ICCS54944.2021.00033(129-132)Online publication date: Dec-2021
  • (2021)Sentiment Evolution in Social Network Based on Joint Pre-training Model2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD49262.2021.9437878(1093-1098)Online publication date: 5-May-2021
  • (2021)Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social MediaIEEE Access10.1109/ACCESS.2021.30736579(61756-61767)Online publication date: 2021
  • (2021)A Survey on Opinion Reason Mining and Interpreting Sentiment VariationsIEEE Access10.1109/ACCESS.2021.30639219(39636-39655)Online publication date: 2021
  • (2021)Tracking sentiment towards news entities from Arabic news on social mediaFuture Generation Computer Systems10.1016/j.future.2021.01.015118(467-484)Online publication date: May-2021
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