Opay-Google-Play-Store-Sentiment-Analysis

This project analyzes user reviews from the Google Play Store to understand customer sentiment towards Opay mobile app. It leverages natural language processing (NLP) techniques and the VADER sentiment analysis tool to categorize reviews as positive, negative, or neutral and visualizing the insights using Power BI.

Data Collection: Fetch user reviews from the Google Play Store using the google-play-scraper library.

Data Cleaning: Preprocess the text data by removing unnecessary characters, converting to lowercase, and handling missing values.

Sentiment Analysis: Utilize VADER to calculate sentiment scores (negative, neutral, positive, compound) for each review.

Categorization: Classify reviews into sentiment categories based on the compound score.

Word Frequency Analysis: Tokenize and lemmatize the text, remove stop words, and count word frequencies for each sentiment category.

Visualization: Create visualizations (e.g., bar charts, word clouds) to gain insights from the sentiment and word frequency data.

Overall Sentiment: The Opay app has a positive sentiment score of 85.53%, with an average rating of 4.28/5 from 258K reviews.

Frequent Feedback:

Rating Distribution:

Explore the full interactive dashboard here.

Enhance Customer Service:

Optimize Transaction Processes:

Network and App Performance:

User Feedback Integration:

Communication and Transparency: