Deriving insights from national happiness indices

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dc.contributor.author Brew, Anthony
dc.contributor.author Greene, Derek
dc.contributor.author Archambault, Daniel
dc.contributor.author Cunningham, Pádraig
dc.date.accessioned 2011-10-18T16:25:17Z
dc.date.available 2011-10-18T16:25:17Z
dc.date.copyright 2011 IEEE en
dc.date.issued 2011-12-11
dc.identifier.isbn 978-1-4673-0005-6 en
dc.identifier.uri http://hdl.handle.net/10197/3243
dc.description Paper presented at the IEEE International Conference on Data Mining series (ICDM'11), December 11th to 14th, 2011, Vancouver, Canada en
dc.description.abstract In online social media, individuals produce vast amounts of content which in effect "instruments" the world around us. Users on sites such as Twitter are publicly broadcasting status updates that provide an indication of their mood at a given moment in time, often accompanied by geolocation information. A number of strategies exist to aggregate such content to produce sentiment scores in order to build a "happiness index". In this paper, we describe such a system based on Twitter that maintains a happiness index for nine US cities. The main contribution of this paper is a companion system called SentireCrowds that allows us to identify the underlying causes behind shifts in sentiment. This ability to analyse the components of the sentiment signal highlights a number of problems. It shows that sentiment scoring on social media data without considering context is difficult. More importantly, it highlights cases where sentiment scoring methods are susceptible to unexpected shifts due to noise and trending memes. en
dc.description.sponsorship Science Foundation Ireland en
dc.format.extent 1131122 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher IEEE en
dc.relation.ispartof 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW) [proceedings] en
dc.relation.requires CASL Research Collection en
dc.relation.requires Clique Research Collection en
dc.rights Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. en
dc.subject Visualisation en
dc.subject Data mining en
dc.subject Social network analysis en
dc.subject Twitter en
dc.subject.lcsh Visualization en
dc.subject.lcsh Data mining en
dc.subject.lcsh Online social networks--Data processing en
dc.subject.lcsh Happiness--Testing en
dc.subject.lcsh Twitter en
dc.title Deriving insights from national happiness indices en
dc.type Conference Publication en
dc.internal.availability Full text available en
dc.internal.webversions http://dx.doi.org/10.1109/ICDMW.2011.61 en
dc.status Peer reviewed en
dc.identifier.doi 10.1109/ICDMW.2011.61 en
dc.neeo.contributor Brew|Anthony|aut| en
dc.neeo.contributor Greene|Derek|aut| en
dc.neeo.contributor Archambault|Daniel|aut| en
dc.neeo.contributor Cunningham|Pádraig|aut| en
dc.description.admin On publication add DOI and link to abstarct in IEEE Xplore - AV 11/10/2011 ti, ke - kpw17/10/11 en


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