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2016 | OriginalPaper | Buchkapitel

Subject-Related Message Filtering in Social Media Through Context-Enriched Language Models

verfasst von : Alexandre Davis, Adriano Veloso

Erschienen in: Transactions on Computational Collective Intelligence XXI

Verlag: Springer Berlin Heidelberg

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Abstract

Efficiently retrieving and understanding messages from social media is challenging, considering that shorter messages are strongly dependent on context. Assuming that their audience is aware of background and real world events, users can shorten their messages without compromising communication. However, traditional data mining algorithms do not account for contextual information. We argue that exploiting context can lead to advancements in the analysis of social media messages. Recall rate increases if context is taken into account, leading to context-aware methods for filtering messages without resorting only to keywords. A novel approach for subject classification of social media messages, using computational linguistics techniques, is proposed, employing both textual and extra-textual (or contextual) information. Experimental analysis over sports-related messages indicates over 50 % improvement in retrieval rate over text-based approaches due to the use of contextual information.

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Titel: Subject-Related Message Filtering in Social Media Through Context-Enriched Language Models
verfasst von: Alexandre Davis
Adriano Veloso
Verlag: Springer Berlin Heidelberg
Buch: Transactions on Computational Collective Intelligence XXI
Print ISBN: 978-3-662-49520-9

Electronic ISBN: 978-3-662-49521-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-662-49521-6_5

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