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        <title>Hashtag Processing for Enhanced Clustering of Tweets</title>
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    <name type="personal">
        <namePart type="given">Dagmar</namePart>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Rich data provided by tweets have beenanalyzed, clustered, and explored in a variety of studies. Typically those studies focus on named entity recognition, entity linking, and entity disambiguation or clustering. Tweets and hashtags are generally analyzed on sentential or word level but not on a compositional level of concatenated words. We propose an approach for a closer analysis of compounds in hashtags, and in the long run also of other types of text sequences in tweets, in order to enhance the clustering of such text documents. Hashtags have been used before as primary topic indicators to cluster tweets, however, their segmentation and its effect on clustering results have not been investigated to the best of our knowledge. Our results with a standard dataset from the Text REtrieval Conference (TREC) show that segmented and harmonized hashtags positively impact effective clustering.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_038</identifier>
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%0 Conference Proceedings
%T Hashtag Processing for Enhanced Clustering of Tweets
%A Gromann, Dagmar
%A Declerck, Thierry
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F gromann-declerck-2017-hashtag
%X Rich data provided by tweets have beenanalyzed, clustered, and explored in a variety of studies. Typically those studies focus on named entity recognition, entity linking, and entity disambiguation or clustering. Tweets and hashtags are generally analyzed on sentential or word level but not on a compositional level of concatenated words. We propose an approach for a closer analysis of compounds in hashtags, and in the long run also of other types of text sequences in tweets, in order to enhance the clustering of such text documents. Hashtags have been used before as primary topic indicators to cluster tweets, however, their segmentation and its effect on clustering results have not been investigated to the best of our knowledge. Our results with a standard dataset from the Text REtrieval Conference (TREC) show that segmented and harmonized hashtags positively impact effective clustering.
%R 10.26615/978-954-452-049-6_038
%U https://doi.org/10.26615/978-954-452-049-6_038
%P 277-283

Markdown (Informal)

[Hashtag Processing for Enhanced Clustering of Tweets](https://doi.org/10.26615/978-954-452-049-6_038) (Gromann & Declerck, RANLP 2017)

• Hashtag Processing for Enhanced Clustering of Tweets (Gromann & Declerck, RANLP 2017)

ACL

• Dagmar Gromann and Thierry Declerck. 2017. Hashtag Processing for Enhanced Clustering of Tweets. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 277–283, Varna, Bulgaria. INCOMA Ltd..