TY - CONF
T1 - Recommendation system of authorities and content based on Twitter for language therapy through data mining techniques
AU - Quisi-Peralta, Diego
AU - Robles-Bykbaev, Vladimir
AU - Lopez-Nores, Martin
AU - Chaglla-Rodriguez, Liliana
AU - Chiluisa-Castillo, Diego
PY - 2019/2/20
Y1 - 2019/2/20
N2 - According to latest estimations of the World Health Organization (WHO), approximately 15% of persons have some disability. From this group, a significant percentage of persons present different kinds of communication and language disorders. Additionally, it is important mentioning that language supports other essential processes in children development, such as learning, skills to interact with his/her peers, and establishing relationships. On the other hand, nowadays exist several digital platforms like blogs, twitter, and in general, social networks, where experts and practitioners contribute with contents related to speech and language therapy. For these reasons, in this paper, we present a recommender system to support the content filtering, identify experts or interest groups related to communication disorders. Our recommender uses data mining techniques to perform the contents filtering, and a clustering approach to classifying the wich twitter contents are related to speech-language therapy area. In order to validate our proposal, we have conducted several tests with the support of a team of experts. The achieved results show that the recommendations of the system are useful, coherent, and understandable.
AB - According to latest estimations of the World Health Organization (WHO), approximately 15% of persons have some disability. From this group, a significant percentage of persons present different kinds of communication and language disorders. Additionally, it is important mentioning that language supports other essential processes in children development, such as learning, skills to interact with his/her peers, and establishing relationships. On the other hand, nowadays exist several digital platforms like blogs, twitter, and in general, social networks, where experts and practitioners contribute with contents related to speech and language therapy. For these reasons, in this paper, we present a recommender system to support the content filtering, identify experts or interest groups related to communication disorders. Our recommender uses data mining techniques to perform the contents filtering, and a clustering approach to classifying the wich twitter contents are related to speech-language therapy area. In order to validate our proposal, we have conducted several tests with the support of a team of experts. The achieved results show that the recommendations of the system are useful, coherent, and understandable.
KW - Language therapy
KW - artificial intelligence
KW - natural language processing
KW - recommendation system
KW - twitter
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85063441228&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85063441228&origin=inward
UR - http://www.mendeley.com/research/recommendation-system-authorities-content-based-twitter-language-therapy-through-data-mining-techniq
U2 - 10.1109/ARGENCON.2018.8646316
DO - 10.1109/ARGENCON.2018.8646316
M3 - Paper
T2 - 2018 IEEE Biennial Congress of Argentina, ARGENCON 2018
Y2 - 6 June 2018 through 8 June 2018
ER -