Twitter data analysis: temporal and term frequency analysis with real-time event

14th International Conference on Science, Engineering and Technology

自然科学;工业技术

Yadav, Garima^1 ; Joshi, Mansi^1 ; Sasikala, R.^1

School of Computer Science and Engineering, VIT University, Vellore

632014, India^1

关键词: Different domains;    Document formats;    Geolocations;    Social media platforms;    Temporal Data;    Term Frequency;    User-generated content;    World wide webs (WWW);   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042081/pdf
DOI  :  10.1088/1757-899X/263/4/042081

来源: IOP

【 摘 要 】

From the past few years, World Wide Web (www) has become a prominent and huge source for user generated content and opinionative data. Among various social media, Twitter gained popularity as it offers a fast and effective way of sharing users' perspective towards various critical and other issues in different domain. As the data is hugely generated on cloud, it has opened doors for the researchers in the field of data science and analysis. There are various domains such as 'Political' domain, 'Entertainment' domain and 'Business' domain. Also there are various APIs that Twitter provides for developers 1) Search API, focus on the old tweets 2) Rest API, focuses on user details and allow to collect the user profile, friends and followers 3) Streaming API, which collects details like tweets, hashtags, geo locations. In our work we are accessing Streaming API in order to fetch real-time tweets for the dynamic happening event. For this we are focusing on 'Entertainment' domain especially 'Sports' as IPL-T20 is currently the trending on-going event. We are collecting these numerous amounts of tweets and storing them in MongoDB database where the tweets are stored in JSON document format. On this document we are performing time-series analysis and term frequency analysis using different techniques such as filtering, information extraction for text-mining that fulfils our objective of finding interesting moments for temporal data in the event and finding the ranking among the players or the teams based on popularity which helps people in understanding key influencers on the social media platform.