An analytics approach to extracting location and sentiment insights from classified social media data

Social networks are becoming vital for people to interact with each other, which in turn represent the production of a huge amount of information that can be useful in many contexts (e.g., medicine, natural disasters, commercial purposes, tourism). Nevertheless, analyzing such Big Data for insights might be difficult if the appropriate processing and analysis tools are not available. In this sense, we propose a framework to analyze social network content by integrating Topic Detection, Sentiment Analysis, and Geolocation. The gathered information is processed using Natural Language Processing methods to extract textual elements that make it possible for each framework component to function as intended. After reading through a stream of posts, the Topic Detection method classifies them and removes any that have nothing to do with the subject being analyzed. Sentiment Analysis component combines Machine Learning, Knowledge Graphs, and Semantic Web techniques, using SPARQL in conjunction with DBpedia and Nominatim. The Geolocation component scans posts and attempts to determine their geographical position. In this study, we implement a proof-of-concept on X (formerly Twitter), called XAF (X Analyzer Framework), to work in the context of natural disasters, to show the efficiency of combining Sentiment Analysis, Geolocation, and Topic Detection, and the possibility to be used in other contexts. We describe the general architecture of XAF and show the performance of each module as well as the holistic solution. Results show that XAF provides a platform to analyze X posts from different perspectives that allows implementing applications able to respond in real time.