Analyzing Sentiment and Trends from about 1 million tweets obtained from Twitter (rebranded as X)
Conducted comprehensive research, implemented advanced analytics, EDA focused on user interaction data in the data sets of 1 million tweets, offering strategic recommendations for engagement optimization. Managed NoSQL (JSON) data from API sources, handling 125 feature columns in the data. Leveraged Python (numpy and pandas) data driven analytics to analyze market trends, NLP techniques and VertexAI Workbench to forecast sentiment analysis on 350,000 unique tweets.