Abstract
Twitter is an irreplaceable source of data for opinion mining, emergency communications, or fact sharing, whose readability is severely limited by the sheer volume of tweets published every day. A method to represent and synthesize the information content of conversations on Twitter in form of semantic maps, from which the main topics and the main orientations of tweeters may easily be read, is proposed hereafter. After a preliminary grouping of tweets in conversations, relevant keywords and Named Entities are extracted, disambiguated and clustered. Annotations are made using extensive knowledge bases and state-of-the-art techniques from Natural Language Processing and Machine Learning. The results are in form of coloured graphs, to be easily interpretable. Several experiments confirm the high understandability and the good adherence to tackled topics of the mapped conversations.
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Acknowledgements
The authors thank Agostino Perasole, Michele Crivellari and Antonio Lo Regio for the help during their bachelor’s degree at the University of Naples “Parthenope”. This work was funded by the University of Naples “Parthenope” (project “Sostegno alla ricerca individuale per il triennio 2015–2017”).
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Ciaramella, A., Maratea, A., Spagnoli, E. (2018). Semantic Maps of Twitter Conversations. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_31
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DOI: https://doi.org/10.1007/978-3-319-56904-8_31
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