Real-Time Twitter Trends Analysis Using Latent Dirichlet Allocation and Machine Learning

Abstract

In today’s world of growing social media usage twitter has become an enormous source of data from public in form of tweets. These tweets can be collect and useful to extract effective meaningful insights ex, public sentiments, choices and opinions about a particular event, product or person, which can prove helpful for business growth, political parties and celebrities to know public choices, sentiments and their reviews over a particular product , person or government decision. But before performing sentiment analysis on tweets data we need to clean and per-process it as tweets data is highly unstructured and noisy for that many methods are there:-data per-processing, stop-words removal, stemming, lemmatization etc. After data cleaning effective meaningful insights can be extracted and sentiment analysis can be performed to extract public opinions and their choices. Twitter data possesses significant power in capturing timely public opinions on a variety of topics, such as preferences for products, political tendencies, and sentiments within the business realm. The extensive user base of this platform provides a wide array of viewpoints, rendering it a valuable resource for comprehending consumer behaviour, political developments, and market outlooks. By employing sentiment analysis, organizations can measure levels of customer contentment, policymakers can evaluate public reception, and corporations can monitor how their brands are perceived to make well-informed decisions. The real-time aspect and high level of user involvement associated with Twitter data render it priceless for shaping strategies related to product innovation, political campaigns, and business expansion efforts. This research paper highlights the power of power BI tool in data visualization of real-time tweets of a user twitter account and builds effective dashboards and also perform sentiment analysis of real-time tweets. Keywords: LDA, Machine Learning, Topic Modelling, Sentiment Analysis, NLP, Social Media.