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dc.contributor.authorUysal, E.
dc.contributor.authorYumusak, S.
dc.contributor.authorOztoprak, K.
dc.contributor.authorDogdu, E.
dc.date.accessioned2020-08-07T12:55:36Z
dc.date.available2020-08-07T12:55:36Z
dc.date.issued2017
dc.identifier10.1145/3077286.3077569
dc.identifier.issn9781450350242 (ISBN)
dc.identifier.urihttp://hdl.handle.net/20.500.12498/2962
dc.description.abstractThe ideas expressed in social media are not always compliant with natural language rules, and the mood and emotion indicators are mostly highlighted by emoticons and emotion specic keywords. There are language independent emotion keywords (e.g. love, hate, good, bad), besides every language has its own particular emotion specific keywords. These keywords can be used for polarity analysis for a particular sentence. In this study, we first created a Turkish dictionary containing emotion specific keywords. Then, we used this dictionary to detect the polarity of tweets that are collected by querying political keywords right before the Turkish general election in 2015. The tweets were collected based on their relatedness with three main categories: the political leaders, ideologies, and political parties. The polarity of these tweets are analyzed in comparison with the election results. © 2017 ACM.
dc.language.isoEnglish
dc.publisherAssociation for Computing Machinery, Inc
dc.source2017 ACM SouthEast Regional Conference, ACMSE 2017
dc.titleSentiment analysis for the social media: A case study for Turkish general elections
dc.typeConference Paper


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