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Sentiment Analysis of Yelp & Trip Advisor Reviews

Overview

This GitHub repository houses the project on conducting sentiment analysis on reviews collected from Yelp and Trip Advisor. The aim is to decipher the sentiment behind user reviews using advanced NLP techniques, thereby uncovering insights into public opinion about local businesses.

Project Objective

The primary goal is to analyze and classify the sentiments of reviews into positive, negative, or neutral categories, providing stakeholders with a deeper understanding of customer feedback and its implications for business reputation and service quality.

Technologies Used

  • Data Manipulation: pandas, numpy
  • Text Processing & Sentiment Analysis: spacy, nltk, transformers
  • Visualization: matplotlib, seaborn, wordcloud
  • Machine Learning Models: BERT, RoBERTa for sentiment classification.

Workflow

  1. Data Collection: Reviews were scraped from Yelp and Trip Advisor using custom scripts.
  2. Preprocessing: Text data was cleaned and preprocessed to remove noise and standardize the format for analysis.
  3. Sentiment Analysis: Applied NLP models to categorize reviews based on sentiment.
  4. Visualization and Analysis: Visualized results using graphs and word clouds to identify trends and patterns in sentiment.
  5. Comparative Study: Compared sentiments across Yelp and Trip Advisor to highlight platform-specific trends.

Installation

Instructions for setting up the project environment:

git clone <https://github.com/narendranathe/Sentiment_Analysis>
cd <https://github.com/narendranathe/Sentiment_Analysis/blob/master/Final.ipynb>

Challenges Encountered

Dynamic Content Handling: Adaptation to constantly changing review data and formats. Model Fine-Tuning: Adjusting parameters for optimal sentiment classification accuracy. Data Preprocessing Complexity: Developing efficient preprocessing pipelines to handle large datasets.

Results & Impact

The analysis revealed significant insights into customer sentiment trends, providing valuable feedback for businesses to improve their services. The comparative analysis between Yelp and Trip Advisor also offered unique perspectives on user engagement across platforms.

Future Enhancements

  • Expand the analysis to include more review platforms.
  • Implement real-time sentiment analysis for up-to-date insights.
  • Explore deeper NLP techniques for nuanced sentiment understanding.

Contributing

Contributions to this project are welcome. Please submit a pull request or open an issue to propose changes or additions.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgements

Thanks to the NLP and data science community for the resources and tools made available for public use.

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