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Identifying breakpoints in public opinion

Published: 25 July 2010 Publication History

Abstract

While polls are traditionally used for observing public opinion, they provide a point snapshot, not a continuum. We consider the problem of identifying breakpoints in public opinion, and propose using micro-blogging sites to capture trends in public opinion. We develop methods to detect changes in public opinion, and find events that cause these changes.
Our experiments show that the proposed methods are able to determine changes in public opinion and extract the major news about the events effectively. We also deploy an application where users can view the important news stories for a continuing event and find the related articles on web.

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  • (2023)Polarity classification on twitter data for classifying sarcasm using clause pattern for sentiment analysisMultimedia Tools and Applications10.1007/s11042-023-14909-w82:21(32789-32825)Online publication date: 2-Mar-2023
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Published In

cover image ACM Conferences
SOMA '10: Proceedings of the First Workshop on Social Media Analytics
July 2010
145 pages
ISBN:9781450302173
DOI:10.1145/1964858
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: 25 July 2010

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Author Tags

  1. emotion corpus
  2. microblogging
  3. opinion mining
  4. sentiment analysis

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Cited By

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  • (2023)A pattern-based approach to detect irony in twitter sentiment analysisi-manager’s Journal on Pattern Recognition10.26634/jpr.10.2.2035410:2(19)Online publication date: 2023
  • (2023)Polarity classification on twitter data for classifying sarcasm using clause pattern for sentiment analysisMultimedia Tools and Applications10.1007/s11042-023-14909-w82:21(32789-32825)Online publication date: 2-Mar-2023
  • (2023)Internet Public Safety Event Grading and Hybrid Storage Based on Multi-feature Fusion for Social Media TextsDatabase Systems for Advanced Applications10.1007/978-3-031-30637-2_38(578-587)Online publication date: 14-Apr-2023
  • (2022)Sentiment Analysis using a Machine Learning Approach in Python2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)10.1109/IC3IOT53935.2022.9768004(1-6)Online publication date: 10-Mar-2022
  • (2022)Sarcasm Over Time and Across Platforms: Does the Way We Express Sarcasm Change?IEEE Access10.1109/ACCESS.2022.317486210(55958-55987)Online publication date: 2022
  • (2022)Sentiment Analysis Through Machine Learning: A ReviewProceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications10.1007/978-981-16-6332-1_52(633-647)Online publication date: 14-Feb-2022
  • (2021)AthPPA: A Data Visualization Tool for Identifying Political Popularity over TwitterInformation10.3390/info1208031212:8(312)Online publication date: 31-Jul-2021
  • (2021)Over a decade of social opinion mining: a systematic reviewArtificial Intelligence Review10.1007/s10462-021-10030-254:7(4873-4965)Online publication date: 1-Oct-2021
  • (2021)A Framework to Capture the Shift in Dynamics of a Multi-phase Protest—A Case Study of Hong Kong ProtestsEmerging Technologies in Data Mining and Information Security10.1007/978-981-15-9774-9_10(95-110)Online publication date: 5-May-2021
  • (2020)Leveraging Personalized Sentiment Lexicons for Sentiment AnalysisProceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval10.1145/3409256.3409850(109-112)Online publication date: 14-Sep-2020
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