🧠 NLP Data Science Project: Analyzing Google Play Store Reviews 📊

📝 Overview

This repository contains code and documentation for an NLP (Natural Language Processing) project that analyzes user reviews from the Google Play Store for two popular mobile games: League of Legends: Wild Rift and Mobile Legends: Bang Bang.

🏗️ Project Structure

• data/: Contains the raw review data downloaded from the Google Play Store.
• notebooks/: Jupyter notebooks for data preprocessing, exploratory data analysis, and modeling.
• src/: Python scripts for data cleaning, feature extraction, and model training.
• results/: Stores intermediate results and final model outputs.
• README.md: This file, providing an overview of the project.

📊 Data Collection

1. Google Play Store Reviews:
   • We scraped user reviews for both games using the Google Play Store API.
   • Each review includes the user's rating, text, and timestamp.

🛠️ Data Preprocessing

1. Cleaning and Tokenization:

   • Removed special characters, emojis, and HTML tags.
   • Tokenized the review text into individual words.

2. Feature Extraction:

   • Extracted features such as sentiment scores, word frequencies, and review length.
   • Created a bag-of-words representation for modeling.

🔍 Exploratory Data Analysis (EDA)

1. Distribution of Ratings:

   • Visualized the distribution of ratings (1 to 5 stars) for both games.
   • Explored any patterns or anomalies.

2. Word Clouds:

   • Generated word clouds to visualize frequently occurring words in positive and negative reviews.

💡 Sentiment Analysis

1. Sentiment Classification:
   • Trained a Naive Bayes classifier to predict sentiment (positive/negative) based on review text.
   • Evaluated model performance using cross-validation.

📈 Topic Modeling (LLMS)

1. Latent Dirichlet Allocation (LDA):
   • Applied LDA to discover latent topics within the reviews.
   • Identified key terms associated with each topic (e.g., gameplay, graphics, bugs).

🌟 Rating Prediction

1. Regression Model:
   • Built a regression model to predict user ratings based on review features.
   • Features include sentiment scores, review length, and topic proportions.

📋 Results

1. Sentiment Distribution:

   • Compared the proportion of positive and negative reviews for each game.
   • Identified areas for improvement based on negative sentiment.

2. Top Keywords:

   • Extracted keywords associated with positive and negative sentiments.
   • Example: "exciting," "smooth controls" (positive) vs. "bugs," "lag" (negative).

3. Topic Insights:

   • Discovered topics related to gameplay, graphics, and user experience.
   • Explored how these topics impact user ratings.

🏁 Conclusion

• League of Legends: Wild Rift:

  • Generally positive sentiment.
  • Strengths: Exciting gameplay, strategy, and team dynamics.
  • Areas for improvement: Address bugs and optimize performance.

• Mobile Legends: Bang Bang:

  • Mixed sentiment.
  • Strengths: Vibrant graphics, memorable characters.
  • Areas for improvement: Balance gameplay and enhance user experience.

🔜 Next Steps

• Investigate correlations between reviews and in-game events (e.g., updates, events, patches).
• Explore more advanced NLP techniques for sentiment analysis and topic modeling.