Real-Time Twitter Trend Analysis Using Big Data Analytics and Machine Learning Techniques

Twitter is a popular microblogging social media, using which its users can share useful information. Keeping a track of user postings and common hashtags allows us to understand what is happening around the world and what are people’s opinions on it. As such, a Twitter trend analysis analyzes Twitte...

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Autores principales:	Anisha P. Rodrigues, Roshan Fernandes, Adarsh Bhandary, Asha C. Shenoy, Ashwanth Shetty, M. Anisha
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/c4703fa35e0b47ffb3c5d3237efbf959
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spelling	oai:doaj.org-article:c4703fa35e0b47ffb3c5d3237efbf9592021-11-08T02:36:52ZReal-Time Twitter Trend Analysis Using Big Data Analytics and Machine Learning Techniques1530-867710.1155/2021/3920325https://doaj.org/article/c4703fa35e0b47ffb3c5d3237efbf9592021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3920325https://doaj.org/toc/1530-8677Twitter is a popular microblogging social media, using which its users can share useful information. Keeping a track of user postings and common hashtags allows us to understand what is happening around the world and what are people’s opinions on it. As such, a Twitter trend analysis analyzes Twitter data and hashtags to determine what topics are being talked about the most on Twitter. Feature extraction and trend detection can be performed using machine learning algorithms. Big data tools and techniques are needed to extract relevant information from continuous steam of data originating from Twitter. The objectives of this research work are to analyze the relative popularity of different hashtags and which field has the maximum share of voice. Along with this, the common interests of the community can also be determined. Twitter trends plan an important role in the business field, marketing, politics, sports, and entertainment activities. The proposed work implemented the Twitter trend analysis using latent Dirichlet allocation, cosine similarity, K means clustering, and Jaccard similarity techniques and compared the results with Big Data Apache SPARK tool implementation. The LDA technique for trend analysis resulted in an accuracy of 74% and Jaccard with an accuracy of 83% for static data. The results proved that the real-time tweets are analyzed comparatively faster in the Big Data Apache SPARK tool than in the normal execution environment.Anisha P. RodriguesRoshan FernandesAdarsh BhandaryAsha C. ShenoyAshwanth ShettyM. AnishaHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Anisha P. Rodrigues Roshan Fernandes Adarsh Bhandary Asha C. Shenoy Ashwanth Shetty M. Anisha Real-Time Twitter Trend Analysis Using Big Data Analytics and Machine Learning Techniques
description	Twitter is a popular microblogging social media, using which its users can share useful information. Keeping a track of user postings and common hashtags allows us to understand what is happening around the world and what are people’s opinions on it. As such, a Twitter trend analysis analyzes Twitter data and hashtags to determine what topics are being talked about the most on Twitter. Feature extraction and trend detection can be performed using machine learning algorithms. Big data tools and techniques are needed to extract relevant information from continuous steam of data originating from Twitter. The objectives of this research work are to analyze the relative popularity of different hashtags and which field has the maximum share of voice. Along with this, the common interests of the community can also be determined. Twitter trends plan an important role in the business field, marketing, politics, sports, and entertainment activities. The proposed work implemented the Twitter trend analysis using latent Dirichlet allocation, cosine similarity, K means clustering, and Jaccard similarity techniques and compared the results with Big Data Apache SPARK tool implementation. The LDA technique for trend analysis resulted in an accuracy of 74% and Jaccard with an accuracy of 83% for static data. The results proved that the real-time tweets are analyzed comparatively faster in the Big Data Apache SPARK tool than in the normal execution environment.
format	article
author	Anisha P. Rodrigues Roshan Fernandes Adarsh Bhandary Asha C. Shenoy Ashwanth Shetty M. Anisha
author_facet	Anisha P. Rodrigues Roshan Fernandes Adarsh Bhandary Asha C. Shenoy Ashwanth Shetty M. Anisha
author_sort	Anisha P. Rodrigues
title	Real-Time Twitter Trend Analysis Using Big Data Analytics and Machine Learning Techniques
title_short	Real-Time Twitter Trend Analysis Using Big Data Analytics and Machine Learning Techniques
title_full	Real-Time Twitter Trend Analysis Using Big Data Analytics and Machine Learning Techniques
title_fullStr	Real-Time Twitter Trend Analysis Using Big Data Analytics and Machine Learning Techniques
title_full_unstemmed	Real-Time Twitter Trend Analysis Using Big Data Analytics and Machine Learning Techniques
title_sort	real-time twitter trend analysis using big data analytics and machine learning techniques
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/c4703fa35e0b47ffb3c5d3237efbf959
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Real-Time Twitter Trend Analysis Using Big Data Analytics and Machine Learning Techniques

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