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A Study on Spatiotemporal Topical Analysis of Twitter Data
L. Dutta, G. Maji,
Published in Springer Verlag
Volume: 937
Pages: 699 - 711
In this new era of Web 2.0, people around the world are expressing their feelings, sentiments, thoughts, daily activities, and local and global events happening around them in different social networking sites like Twitter, Facebook, etc. This generates vast amount of data in social media by registered users which are geographical and temporal information-oriented. This rich data could be potentially useful information and is being extensively used nowadays for different applications like user’s sentiment analysis, product or service reviews, real-time information extraction like traffic, disaster reporting, personalized message or user recommendation, and other areas. Extracting topic distribution from social media in spatial and temporal dimensions is an important research area. Hence, our focus of this study is on discussing various topical modeling techniques and their uses in different recent research works. This chapter gives a brief overview of the recent updates of spatiotemporal topical analysis using Twitter data. This study categorizes a large number of recent studies and articles in relevant area to get a summarized view of the state of the art in this field. This survey will help researchers, who are new to the domain, and provide a quick baseline for further research. © Springer Nature Singapore Pte Ltd 2020.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing
PublisherData powered by TypesetSpringer Verlag