Introduction

The Sentiment Analysis of a Phoenix Neo Bank is a project aimed at analyzing customer reviews and sentiments associated with the various features of the Neo Bank's mobile app and website. By applying natural language processing techniques and sentiment analysis algorithms, this project provides insights into customer satisfaction levels, identifies key product features, and visualizes sentiment trends.

Objective

The objective of this project was to understand customers' opinions and sentiments regarding Phoenix Neo Bank's, including its mobile app and website. By analyzing a collection of customer reviews, the project aimed to identify positive and negative sentiments, determine the most frequently mentioned product features, and assess overall customer satisfaction.

Data Collection and Analysis:

• A diverse dataset of customer reviews was collected, covering aspects such as user interface, stability, navigation, customer support, performance, security, functionality, transaction experience, and content. Each review was associated with sentiment scores, ratings, and key phrases representing important aspects mentioned in the reviews.

• The data was preprocessed, including tasks such as text cleaning, tokenization, and sentiment scoring. The sentiment scores were assigned based on the sentiment intensity of the review texts, providing a measure of the positive or negative sentiment expressed in each review. Ratings and ratings by feature were also included to provide additional insights into customer satisfaction levels

Visualization and Insights

• Sentiment Distribution: A pie chart showing the distribution of positive, negative, and neutral sentiments in the customer reviews.


• Sentiment by Product Feature: A stacked bar chart displaying sentiment scores for different product features, allowing for a comparison of sentiment levels across features.


• Word Cloud: A visually appealing word cloud representing the most frequently mentioned key phrases extracted from the customer reviews, with the size of each phrase indicating its importance or frequency.


• Sentiment Trend: A line chart illustrating the sentiment trends over time, providing insights into the overall customer satisfaction levels and any changes over different periods.

Conclusion

The project successfully showcased the power of sentiment analysis and data visualization techniques in understanding customer sentiments, identifying areas for improvement, and informing decision-making processes.
This project demonstrates my ability to collect and analyze customer data, apply natural language processing techniques, and visualize the results using Power BI. By leveraging sentiment analysis, businesses like Neo banks can gain valuable insights into customer sentiments, improve their products, and enhance customer satisfaction