Effective Information Retrieval Framework for Twitter Data Analytics

Effective Information Retrieval Framework for Twitter Data Analytics

Ravindra Kumar Singh
Copyright: © 2023 |Pages: 21
DOI: 10.4018/IJIRR.325798
Article PDF Download
Open access articles are freely available for download

Abstract

The widespread adoption of opinion mining and sentiment analysis in higher cognitive processes encourages the need for real time processing of social media data to capture the insights about user's sentiment polarity, user's opinions, and current trends. In recent years, lots of studies were conducted around the processing of data to achieve higher accuracy. But reducing the time of processing still remained challenging. Later, big data technologies came into existence to solve these challenges but those have its own set of complexities along with having hardware deadweight on the system. The contribution of this article is to touch upon mentioned challenges by presenting a climbable, quick and fault tolerant framework to process real-time data to extract hidden insights. This framework is versatile enough to support batch processing along with real time data streams in parallel and distributed environment. Experimental analysis of proposed framework on twitter posts concludes it as quicker, robust, fault tolerant, and comparatively more accurate with traditional approaches.
Article Preview
Top

2. Background

Social media analytics have gained foremost attention (Tsantarliotis & Pitoura, 2017) since last couple of years in researcher’s world for sentiment analysis (Kane et al., 2014) and opinion mining (Maynard et al., 2012). Researchers are more focused on real-time data (Jose & Chooralil, 2015), by using streaming application programming interface (API) of social media platforms like twitter (Tweepy, ), facebook, reddit, etc. to grasp the current trends and public opinions. Social media networking portals are operating on ideology of web 2.0 that defines that data would be created and updated by the users in collaborative manner rather than just being published by individuals who owns it (Kaplan & Haenlein, 2010). Therefore social media data are vague and not bound by any rule or specified formats (Hogenboom et al., 2011), so the research on this data arises the need of some framework to better define the steps of processing and could minimize the complexities in the research.

Complete Article List

Search this Journal:
Reset
Volume 14: 1 Issue (2024)
Volume 13: 1 Issue (2023)
Volume 12: 4 Issues (2022): 3 Released, 1 Forthcoming
Volume 11: 4 Issues (2021)
Volume 10: 4 Issues (2020)
Volume 9: 4 Issues (2019)
Volume 8: 4 Issues (2018)
Volume 7: 4 Issues (2017)
Volume 6: 4 Issues (2016)
Volume 5: 4 Issues (2015)
Volume 4: 4 Issues (2014)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing