TempTracker: A Service Oriented Temporal Natural Language Processing Based Tool for Document Data Characterization and Social Network Analysis
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Onur Can Sert Information Technologies, Zalando, Ireland Ltd This email address is being protected from spambots. You need JavaScript enabled to view it. |
Sibel Tariyan Özyer Department of Computer Engineering, Ankara Medipol University, Turkey This email address is being protected from spambots. You need JavaScript enabled to view it. |
Deniz Bestepe Department of Computer Engineering, Istanbul Medipol University, Turkey This email address is being protected from spambots. You need JavaScript enabled to view it. |
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Abstract: With the advent of Web 2.0 based technology, news sites and micro-blog sites have become popular and have attracted the attention of people around the world. Existing textual data captured by these sites is highly beneficial for extracting (a) new information to analyze, and (b) temporal course of change in entities, topics and sentiment for differing granularities. This has been demonstrated by the study described in this paper. After collecting the data, several directions have been investigated in order to demonstrate its effectiveness under the umbrella of entity extraction, topic and sentiment analysis using Natural Language Processing (NLP) tools, temporal social media analysis, and time varying trend results of entity and sentiment aspect of entities. A service-based architecture has been proposed to process text data with NLP tools and to enrich the data. Text data is collected and processed via NLP tools. It is retrieved upon request for data analysis. The reported results illustrate the applicability and effectiveness of the conducted study.
Keywords: Natural language processing, entity recognition, sentiment analysis, social network analysis.
Received August 7, 2021; accepted October 7, 2021
https://doi.org/10.34028/iajit/19/3/8