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Mining future spatiotemporal events and their sentiment from online news articles for location-aware recommendation system

Authors:

Shen-Shyang Ho,

Mike Lieberman,

Hanan SametAuthors Info & Claims

MobiGIS '12: Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems

Pages 25 - 32

https://doi.org/10.1145/2442810.2442816

Published: 06 November 2012 Publication History

Abstract

The future-related information mining task for online web resources such as news articles and blogs has been getting more attention due to its potential usefulness in supporting individual's decision making in a world where massive new data are generated daily. Instead of building a data-driven model to predict the future, one extracts future events from these massive data with high probability that they occur at a future time and a specific geographic location. Such spatiotemporal future events can be utilized by a recommender system on a location-aware device to provide localized future event suggestions.

In this paper, we describe a systematic approach for mining future spatiotemporal events from web; in particular, news articles. In our application context, a valid event is defined both spatially and temporally. The mining procedure consists of two main steps: recognition and matching. For the recognition step, we identify and resolve toponyms (geographic location) and future temporal patterns. In the matching step, we perform spatiotemporal disambiguation, de-duplication, and pairing. To provide more useful future event guidance, we attach to each event a sentiment linguistic variable: positive, negative, or neutral, so that one may use these extracted event information for recommendation purposes in the form of "avoid Event A" or "avoid geographic location L at time T" or "attend Event B" based on the event sentiment. The identified future event consists of its geographic location, temporal pattern, sentiment variable, news title, key phrase, and news article URL. Experimental results on 3652 news articles from 21 online new sources collected over a 2-week period in the Greater Washington area are used to illustrate some of the critical steps in our mining procedure.

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Cited By

Gupta USharma R(2023)A Survey of Event Detection Techniques in Intelligent IoT System2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10307841(1-6)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICCCNT56998.2023.10307841
Kumar MSingh PMaurya MShivhare A(2023)A survey on event detection approaches for sensor based IoTInternet of Things10.1016/j.iot.2023.10072022(100720)Online publication date: Jul-2023
https://doi.org/10.1016/j.iot.2023.100720
Fernandes EMoro SCortez P(2023)Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunitiesExpert Systems with Applications10.1016/j.eswa.2023.119795221(119795)Online publication date: Jul-2023
https://doi.org/10.1016/j.eswa.2023.119795
Show More Cited By

Index Terms

Mining future spatiotemporal events and their sentiment from online news articles for location-aware recommendation system
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

MobiGIS '12: Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems

November 2012

112 pages

ISBN:9781450316996

DOI:10.1145/2442810

Conference Chair:
Chi-Yin Chow
City University of Hong Kong, Hong Kong

Copyright © 2012 ACM.

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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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

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Published: 06 November 2012

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SIGSPATIAL'12: SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems

November 6, 2012

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Cited By

Gupta USharma R(2023)A Survey of Event Detection Techniques in Intelligent IoT System2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10307841(1-6)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICCCNT56998.2023.10307841
Kumar MSingh PMaurya MShivhare A(2023)A survey on event detection approaches for sensor based IoTInternet of Things10.1016/j.iot.2023.10072022(100720)Online publication date: Jul-2023
https://doi.org/10.1016/j.iot.2023.100720
Fernandes EMoro SCortez P(2023)Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunitiesExpert Systems with Applications10.1016/j.eswa.2023.119795221(119795)Online publication date: Jul-2023
https://doi.org/10.1016/j.eswa.2023.119795
Yu MBambacus MCervone GClarke KDuffy DHuang QLi JLi WLi ZLiu QResch BYang JYang C(2020)Spatiotemporal event detection: a reviewInternational Journal of Digital Earth10.1080/17538947.2020.173856913:12(1339-1365)Online publication date: 9-Mar-2020
https://doi.org/10.1080/17538947.2020.1738569
Sundermann CDomingues MSinoara RMarcacini RRezende S(2019)Using Opinion Mining in Context-Aware Recommender Systems: A Systematic ReviewInformation10.3390/info1002004210:2(42)Online publication date: 28-Jan-2019
https://doi.org/10.3390/info10020042
Kefalas PSymeonidis PManolopoulos Y(2018)Recommendations based on a heterogeneous spatio-temporal social networkWorld Wide Web10.1007/s11280-017-0454-021:2(345-371)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s11280-017-0454-0
Sacco DMotta GYou LBertolazzo NCarini FMa T(2017)Smart cities, urban sensing, and big data: mining geo-location in social networksBig Data and Smart Service Systems10.1016/B978-0-12-812013-2.00005-8(59-84)Online publication date: 2017
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Samet HMitra N(2016)Sorting in spaceSIGGRAPH ASIA 2016 Courses10.1145/2988458.2988503(1-42)Online publication date: 28-Nov-2016
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Kefalas PSymeonidis PManolopoulos Y(2016)A Graph-Based Taxonomy of Recommendation Algorithms and Systems in LBSNsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.249634428:3(604-622)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1109/TKDE.2015.2496344
Volkova SChetviorkin IArendt DVan Durme B(2016)Contrasting Public Opinion Dynamics and Emotional Response During CrisisSocial Informatics10.1007/978-3-319-47880-7_19(312-329)Online publication date: 23-Oct-2016
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