Sentiment analysis helps understand people's emotions in text. In this project, I analyzed tweets about Zenith Bank. Here is the process:
Data Collection: Gathered tweets using the Twitter API with the help of the Tweepy library. Data Scraping/Mining: Extracted relevant tweets using specific hashtags and keywords mentioning Zenith bank. Data Preparation: Cleaned and organized the data using the necessary Python libraries. Data Exploration: Examined the data to find patterns and trends, again using Pandas for data manipulation and Matplotlib/Seaborn for visualization. Analysis: I finally onducted an explanatory analysis using PowerBI to create visual dashboards that shows the results.
This project aims to help Zenith Bank's staff and management improve their services, as my analysis shows that many customers are not happy.
Customer Feedback: The analysis revealed significant customer dissatisfaction. Recommendations: These insights can help Zenith Bank staff and management improve their services based on customer feedback.
