Authors: Nantia Makrynioti; Andreas Grivas; Christos Sardianos; Nikos Tsirakis; Iraklis Varlamis; Vasilis Vassalos; Vassilis Poulopoulos; Panagiotis Tsantilas
Addresses: Department of Informatics, Athens University of Economics and Business, Greece ' Institute of Informatics and Telecommunications, NCSR 'Demokritos', Greece ' Department of Informatics and Telematics, Harokopio University of Athens, Greece ' Palo Services Ltd., Corinthia, Greece ' Department of Informatics and Telematics, Harokopio University of Athens, Greece ' Department of Informatics, Athens University of Economics and Business, Greece ' Palo Services Ltd., Corinthia, Greece ' Palo Services Ltd., Corinthia, Greece
Abstract: PaloPro is a platform that aggregates textual content from social media and news sites in different languages, analyses them using a series of text mining algorithms and provides advanced analytics to journalists and social media marketers. The platform capitalises on the abundance of social media sources and the information they provide for persons, products and events. In order to handle huge amounts of multilingual data that are collected continuously, we have adopted language independent techniques at all levels and from an engineering point of view, we have designed a system that takes advantage of parallel distributed computing technologies and cloud infrastructure. Different systems handle data aggregation, data processing and knowledge extraction and others deal with the integration and visualisation of knowledge. In this paper, we focus on two important text mining tasks, named entity recognition from texts and sentiment analysis to extract the sentiment associated with the corresponding identified entities.
Keywords: text mining; social media analysis; named entity recognition; NER; sentiment analysis; opinion mining; knowledge extraction; big social data; big data; news sites; cloud computing; parallel computing; distributed computing; data aggregation; data processing; knowledge integration; knowledge visualisation.
International Journal of Big Data Intelligence, 2017 Vol.4 No.1, pp.3 - 22
Received: 04 Jul 2015
Accepted: 01 Nov 2015
Published online: 26 Dec 2016 *