Abstract
ABSTRACTThe 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.
Highlights
With the widespread use of social networks and the developments in information technology growing at a fast pace, access to information has become easier, and it has become increasingly important to obtain useful and clear information from such networks
Social network-based intelligence is an indispensable technique for venue owners to measure customer satisfaction
We anticipate that our tool will be an important analytical tool for venue owners to determine how their venue is perceived on social media, to intervene quickly to correct deficiencies at times of crisis, to determine the success rates of products, and to better position their businesses against competitors
Summary
With the widespread use of social networks and the developments in information technology growing at a fast pace, access to information has become easier, and it has become increasingly important to obtain useful and clear information from such networks. The rapid increase in the amount of data has caused an increase in the amount of non-structural data. As long as it is not processed, data is stored as a meaningless mass in databases, and it becomes difficult for people to discover knowledge [1,2]. Text mining uses uniform texts that are obtained through natural language processing rather than databases with a known or well-formed dataset. The language of the text and the meaning that it carries may vary according to purpose. Expressions involving complex structures should be classified in a meaningful way using natural language processing techniques. The inferred view may be the mood, the decision, or the thought that the user wants to express about a subject
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