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Comparing Methods to Retrieve Tweets: a Sentiment Approach

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Comparing Methods to Retrieve Tweets: a Sentiment Approach

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dc.contributor.author Schlosser, Stephan es_ES
dc.contributor.author Toninelli, Daniele es_ES
dc.contributor.author Cameletti, Michela es_ES
dc.date.accessioned 2020-09-08T12:13:12Z
dc.date.available 2020-09-08T12:13:12Z
dc.date.issued 2020-05-12
dc.identifier.isbn 9788490488324
dc.identifier.uri http://hdl.handle.net/10251/149605
dc.description.abstract [EN] In current times Internet and social media have become almost unavoidabletools to support research and decision making processes in various fields.Nevertheless, the collection and use of data retrieved from these types ofsources pose different challenges. In a previous paper we compared theefficiency of three alternative methods used to retrieve geolocated tweets overan entire country (United Kingdom). One method resulted as the bestcompromise in terms of both the effort needed to set it and quantity/quality ofdata collected. In this work we further check, in term of content, whether thethree compared methods are able to produce “similar information”. Inparticular, we aim at checking whether there are differences in the level ofsentiment estimated using tweets coming from the three methods. In doing so,we take into account both a cross-section and a longitudinal perspective. Ourresults confirm that our current best option does not show any significantdifference in the sentiment, producing globally scores in between the scoresobtained using the two alternative methods. Thus, such a flexible and reliablemethod can be implemented in the data collection of geolocated tweets in othercountries and for other studies based on the sentiment analysis. es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Web data es_ES
dc.subject Internet data es_ES
dc.subject Big data es_ES
dc.subject Qca es_ES
dc.subject Pls es_ES
dc.subject Sem es_ES
dc.subject Conference es_ES
dc.subject Social media data collection methods es_ES
dc.subject Twitter data es_ES
dc.subject Sentiment Analysis es_ES
dc.subject Social network es_ES
dc.subject Geographical studies es_ES
dc.title Comparing Methods to Retrieve Tweets: a Sentiment Approach es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/CARMA2020.2020.11653
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Schlosser, S.; Toninelli, D.; Cameletti, M. (2020). Comparing Methods to Retrieve Tweets: a Sentiment Approach. Editorial Universitat Politècnica de València. 299-306. https://doi.org/10.4995/CARMA2020.2020.11653 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics es_ES
dc.relation.conferencedate Julio 08-09,2020 es_ES
dc.relation.conferenceplace Valencia, Spain es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/CARMA/CARMA2020/paper/view/11653 es_ES
dc.description.upvformatpinicio 299 es_ES
dc.description.upvformatpfin 306 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela OCS\11653 es_ES


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