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Title
Leveraging topic extraction for hashtag recommendations / Georg Schneider
AuthorSchneider, Georg
CensorZangerle, Eva
Thesis advisorZangerle, Eva
DescriptionIV, 67 Seiten : 1 CD-ROM ; Diagramme
Institutional NoteUniversität Innsbruck, Univ., Masterarbeit, 2015
Date of SubmissionJuly 2015
LanguageEnglish
Document typeMaster Thesis
Keywords (EN)hashtag / recommender / recommender system / recommendation / hashtag recommendation / hashtag recommender / hybrid recommender / twitter / Maui / topic extraction / keyword extraction
URNurn:nbn:at:at-ubi:1-4828 Persistent Identifier (URN)
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 The work is publicly available
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Leveraging topic extraction for hashtag recommendations [0.78 mb]
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Abstract (German)

The microblogging service Twitter uses hashtags to tag and categorize messages. To automatically recommend appropriate hashtags, we analyze the text contained in websites linked in a Twitter message. This paper compares and analyzes various approaches and algorithms for topic extraction and seeks to optimize and use them in order to generate keywords for a Twitter message. These keywords, along with the text of the input message and any hashtags in it, are then used by six recommender algorithms and three hybrid recommenders to find and suggest suitable hashtags.

Abstract (English)

The microblogging service Twitter uses hashtags to tag and categorize messages. To automatically recommend appropriate hashtags, we analyze the text contained in websites linked in a Twitter message. This paper compares and analyzes various approaches and algorithms for topic extraction and seeks to optimize and use them in order to generate keywords for a Twitter message. These keywords, along with the text of the input message and any hashtags in it, are then used by six recommender algorithms and three hybrid recommenders to find and suggest suitable hashtags.

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CC-BY-NC-License (4.0)Creative Commons Attribution - NonCommercial 4.0 International License