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Topic Sensitive User Clustering Using Sentiment Score and Similarity Measures: Big Data and Social Network

Topic Sensitive User Clustering Using Sentiment Score and Similarity Measures: Big Data and Social Network

Bharat Tidke, Rupa G. Mehta, Dipti P. Rana, Hullash Jangir
Copyright: © 2020 |Volume: 15 |Issue: 2 |Pages: 12
ISSN: 1548-1093|EISSN: 1548-1107|EISBN13: 9781799803973|DOI: 10.4018/IJWLTT.2020040103
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MLA

Tidke, Bharat, et al. "Topic Sensitive User Clustering Using Sentiment Score and Similarity Measures: Big Data and Social Network." IJWLTT vol.15, no.2 2020: pp.34-45. http://doi.org/10.4018/IJWLTT.2020040103

APA

Tidke, B., Mehta, R. G., Rana, D. P., & Jangir, H. (2020). Topic Sensitive User Clustering Using Sentiment Score and Similarity Measures: Big Data and Social Network. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 15(2), 34-45. http://doi.org/10.4018/IJWLTT.2020040103

Chicago

Tidke, Bharat, et al. "Topic Sensitive User Clustering Using Sentiment Score and Similarity Measures: Big Data and Social Network," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT) 15, no.2: 34-45. http://doi.org/10.4018/IJWLTT.2020040103

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Abstract

Social media data (SMD) is driven by statistical and analytical technologies to obtain information for various decisions. SMD is vast and evolutionary in nature which makes traditional data warehouses ill suited. The research aims to propose and implement novel framework that analyze tweets data from online social networking site (OSN; i.e., Twitter). The authors fetch streaming tweets from Twitter API using Apache Flume to detect clusters of users having similar sentiment. Proposed approach utilizes scalable and fault tolerant system (i.e., Hadoop) that typically harness HDFS for data storage and map-reduce paradigm for data processing. Apache Hive is used to work on top of Hadoop for querying data. The experiments are performed to test the scalability of proposed framework by examining various sizes of data. The authors' goal is to handle big social data effectively using cost-effective tools for fetching as well as querying unstructured data and algorithms for analysing scalable, uninterrupted data streams with finite memory and resources.