Abstract
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.
Highlights
Twitter is a popular social networking site where millions of people tweet every second about various topics related to society, politics, sports, entertainment, and many more
For the Twitter trend analysis, the dataset can be considered as the set of documents where each document will be a tweet
In the case of dynamic analysis, data is streamed on a real-time basis, and analysis is performed at the same time by using SPARK structured streaming
Summary
Twitter is a popular social networking site where millions of people tweet every second about various topics related to society, politics, sports, entertainment, and many more. Hashtags are words or phrases which are prefixed with “#,” and user mention means mentioning other people, companies, brands, or precisely other Twitter users in the tweet by using the “@” symbol at the beginning of their user name. Tweets help people to understand how others feel about different ongoing events, government policies, sports tournaments, etc. Twitter trends play an important role in the process of decision-making by different organizations and companies. The main motivation for the Twitter trend analysis is to identify the recent trends happening across the world using big data machine learning techniques. This will help to analyze what has happened in the past and what may happen in the future. It helps to track customer trends and interests especially what customers like, what their behaviors are, and how this changes over the time
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