Good and bad events: Combining network-based event detection with sentiment analysis
dc.contributor.author | Moutidis, I | |
dc.contributor.author | Williams, HTP | |
dc.date.accessioned | 2020-08-07T07:44:59Z | |
dc.date.issued | 2020-08-05 | |
dc.description.abstract | The huge volume and velocity of media content published on the Web presents a substantial challenge to human analysts. In prior work, we developed a system (network event detection, NED) to assist analysts by detecting events within high-volume news streams in real time. NED can process a heterogeneous stream of news articles or social media user posts, combining text mining and network analysis to detect breaking news stories and generate an easy-to-understand event summary. In this paper, we expand the NED event detection and summarisation approach in two ways. First, we introduce a new approach to named entity disambiguation for tweets, which contain minimal information due to brevity. Second, we apply sentiment analysis techniques to documents associated with a detected event to characterise the event as either broadly ‘positive’ or ‘negative’ based on media portrayal. Our expansion focuses on Twitter streams since Twitter has become an important news dissemination platform and is often the site where emerging events are first seen. To test the extended methodology, we apply it here to three data sets related to political elections in the UK and the USA. The addition of sentiment analysis to the NED event detection methodology improves the insight gained by the user by allowing quick evaluation of the perceived impact of an event. This approach may have potential applications in domains where public sentiment is relevant to decision-making around events, such as financial markets and politics. | en_GB |
dc.description.sponsorship | Adarga Ltd. | en_GB |
dc.description.sponsorship | Turing Institute | en_GB |
dc.description.sponsorship | University of Exeter | en_GB |
dc.identifier.citation | Vol. 10, article 64 | en_GB |
dc.identifier.doi | 10.1007/s13278-020-00681-4 | |
dc.identifier.uri | http://hdl.handle.net/10871/122367 | |
dc.language.iso | en | en_GB |
dc.publisher | Springer | en_GB |
dc.rights | © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | en_GB |
dc.subject | Natural language processing | en_GB |
dc.subject | Network analysis | en_GB |
dc.subject | Sentiment analysis | en_GB |
dc.subject | Social media | en_GB |
dc.subject | Topic modelling | en_GB |
dc.title | Good and bad events: Combining network-based event detection with sentiment analysis | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-08-07T07:44:59Z | |
dc.description | This is the final version. Available on open access from Springer via the DOI in this record | en_GB |
dc.identifier.eissn | 1869-5469 | |
dc.identifier.journal | Social Network Analysis and Mining | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2020-07-27 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2020-07-27 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-08-06T17:06:23Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2020-08-07T07:45:04Z | |
refterms.panel | B | en_GB |
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Except where otherwise noted, this item's licence is described as © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.