Original scientific paper
https://doi.org/10.1080/00051144.2018.1531214
A real-time social network-based knowledge discovery system for decision making
Asım Sinan Yüksel
; Computer Engineering Department, Süleyman Demirel University, Isparta, Turkey
Fatma Gülşah Tan
; Vocational School Computer Programming Department, Celal Bayar University Kırkağaç, Manisa, Turkey
Abstract
The increasing amount of data in social networks has complicated data processing and interpretation. Therefore, intelligent decision-support mechanisms that have the ability to automatically extract meaning from data and interpret the opinions of people in real time have become
inevitable. In this study, an intelligent multilingual decision support system was implemented, and a new algorithm that employs text mining and sentiment analysis techniques was developed to automatically interpret the opinions of social network users about the places they plan to visit. The system can be used as a baseline for sentiment analysis in social networks and can be adapted to build new systems. In this study, we set our main focus on Turkish language and
show the applicability of our approach for other languages through the experiments for English language. The dataset required for the implementation of text mining techniques was created based on the venue recommendations shared on Foursquare social media platform. As a result, a contribution was made to the way the social network users make decisions without reading thousands of recommendations. Our results show that the developed system achieves classification accuracy of 84.49% for Turkish and 95% for English. Finally, the most liked or disliked foods/beverages are correctly identified for 107 out of 128 venues.
Keywords
Decision support system; knowledge discovery; social networks; text mining; natural language processing; sentiment analysis
Hrčak ID:
225200
URI
Publication date:
12.12.2018.
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