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Application with sentiment analysis and topic modeling

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“Peper Analysis” application makes a quick and user-friendly analysis of the neighborhoods in Sofia in the form of a heat map for tourists to find a suitable place to stay in Sofia by searching for different subjective criteria.

The solution is implemented through web scraping (Selenium), sentiment analysis (TextBlob) of the acquired data, analysis (NLTK), topic modeling and word embedding (GloVe).

GitHub Project:
https://github.com/VIVelev/Peper-Analysis

Team: Victor Velev, Vladislav Georgiev, Kaloyan Madzhunov, Martin Dacev, Peter Milev, and Telerik Arsov

Published in: Data & Analytics
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Application with sentiment analysis and topic modeling

  1. 1. Viktor Velev, Vladislav Georgiev, Kaloyan Madjunov Martin Datsev, Petar Milev, Telerik Arsov
  2. 2. THE PROBLEM • The difficult tourists orientation in Sofia • The difficulty of finding a place to live
  3. 3. THE SOLUTION • Fast user friendly analysis of Sofia • Population density • Sport facilities • Best quarters to live in
  4. 4. THE TECHNOLOGIES • NLTK • NumPy • Selenium • GloVe – Stanford NLP • GenSim • Pandas • TextBlob
  5. 5. THE DATA ASSIMILATION • Airbnb API • Tripadvisor API • Dataset with a polyon of the quarters in Sofia
  6. 6. EXPLORING THE DATA • Sentiment analysis • Topic modelling • Scoring locations by topics • Categorical Search
  7. 7. Sentiment Analysis
  8. 8. Topic Modelling
  9. 9. Categorical Search - demo
  10. 10. Categorical Search - implementation
  11. 11. A HUMAN-LIKE SEARCH • Natural input • Search terms extraction • Comparing by meaning
  12. 12. TIME FOR DEMO github.com/VIVelev/Peper-Analysis
  13. 13. github.com/VIVelev/Peper-Analysis

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