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Tracking Sentiment by Time Series Analysis

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SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

Pages 1037 - 1040

https://doi.org/10.1145/2911451.2914702

Published: 07 July 2016 Publication History

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Abstract

In recent years social media have emerged as popular platforms for people to share their thoughts and opinions on all kind of topics. Tracking opinion over time is a powerful tool that can be used for sentiment prediction or to detect the possible reasons of a sentiment change. Understanding topic and sentiment evolution allows enterprises or government to capture negative sentiment and act promptly. In this study, we explore conventional time series analysis methods and their applicability on topic and sentiment trend analysis. We use data collected from Twitter that span over nine months. Finally, we study the usability of outliers detection and different measures such as sentiment velocity and acceleration on the task of sentiment tracking.

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Cited By

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Vanderkooi DMashatan ATuretken O(2023)Introducing technological disruption: how breaking media attention on corporate events impacts online sentimentJournal of Business Analytics10.1080/2573234X.2023.22740887:2(63-82)Online publication date: 31-Oct-2023
https://doi.org/10.1080/2573234X.2023.2274088
Wang YChen ZFu C(2022)Synergy Masks of Domain Attribute Model DaBERT: Emotional Tracking on Time-Varying Virtual Space CommunicationSensors10.3390/s2221845022:21(8450)Online publication date: 3-Nov-2022
https://doi.org/10.3390/s22218450
Tao XShannon-Honson ADelaney PLi LDann CLi YXie H(2022)Data Analytics on Online Student Engagement Data for Academic Performance ModelingIEEE Access10.1109/ACCESS.2022.320895310(103176-103186)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3208953
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Index Terms

Tracking Sentiment by Time Series Analysis
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Time series analysis

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Published In

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

July 2016

1296 pages

ISBN:9781450340694

DOI:10.1145/2911451

General Chairs:
Raffaele Perego
ISTI-CNR, Italy
,
Fabrizio Sebastiani
Qatar Computing Research Institute, HBKU, Qatar
,
Program Chairs:
Javed Aslam
Northeastern University, US
,
Ian Ruthven
University of Strathclyde, UK
,
Justin Zobel
University of Melbourne, Australia

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2016

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SIGIR '16

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SIGIR

SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval

July 17 - 21, 2016

Pisa, Italy

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SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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Vanderkooi DMashatan ATuretken O(2023)Introducing technological disruption: how breaking media attention on corporate events impacts online sentimentJournal of Business Analytics10.1080/2573234X.2023.22740887:2(63-82)Online publication date: 31-Oct-2023
https://doi.org/10.1080/2573234X.2023.2274088
Wang YChen ZFu C(2022)Synergy Masks of Domain Attribute Model DaBERT: Emotional Tracking on Time-Varying Virtual Space CommunicationSensors10.3390/s2221845022:21(8450)Online publication date: 3-Nov-2022
https://doi.org/10.3390/s22218450
Tao XShannon-Honson ADelaney PLi LDann CLi YXie H(2022)Data Analytics on Online Student Engagement Data for Academic Performance ModelingIEEE Access10.1109/ACCESS.2022.320895310(103176-103186)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3208953
Nguyen JArmisen AAgell NSaz‐Carranza Á(2022)Comparing global news sentiment using hesitant linguistic termsInternational Journal of Intelligent Systems10.1002/int.2257937:4(2868-2884)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1002/int.22579
Wu JShang LGao X(2021)Sentiment Time Series Calibration for Event DetectionIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2021.309665329(2407-2420)Online publication date: 14-Jul-2021
https://dl.acm.org/doi/10.1109/TASLP.2021.3096653
Wang XHan CHu JZhang XLv HHuang S(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
https://doi.org/10.1109/CSCWD49262.2021.9437878
Alattar FShaalan K(2021)A Survey on Opinion Reason Mining and Interpreting Sentiment VariationsIEEE Access10.1109/ACCESS.2021.30639219(39636-39655)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3063921
Ding YKorolov R(Al) Wallace WWang X(2021)How are sentiments on autonomous vehicles influenced? An analysis using Twitter feedsTransportation Research Part C: Emerging Technologies10.1016/j.trc.2021.103356131(103356)Online publication date: Oct-2021
https://doi.org/10.1016/j.trc.2021.103356
Wu JQiu BShang L(2021)A Calibration Method for Sentiment Time Series by Deep ClusteringPRICAI 2021: Trends in Artificial Intelligence10.1007/978-3-030-89363-7_1(3-16)Online publication date: 8-Nov-2021
https://dl.acm.org/doi/10.1007/978-3-030-89363-7_1
Wu ZHuang SZhang RLi L(2020)Video Review Analysis via Transformer-Based Sentiment Change Detection2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR49039.2020.00074(330-335)Online publication date: Aug-2020
https://doi.org/10.1109/MIPR49039.2020.00074
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