NLP consulting project: defining a data-driven strategy for the Londonese restaurant Bokan 37

This project has five main steps:

• Data Collection
• Data Cleaning
• Word Embedding
• Topic Extraction
• Sentiment Analysis

Setup

git clone https://github.com/hehlinge42/nlp_consulting_project.git
cd nlp_consulting_project
pip install -r requirements.txt

Architecture

Session 1: TripAdvisor scraper

• Tools to scrap TripAdvisor's UK website (https://www.tripadvisor.co.uk/) for restaurants and their associated reviews made by different users.
• cd scraper
• See dedicated README in the folder.

Session 2: Data cleaner

• Tool to clean and tokenize the reviews scraped from TripAdvisor.
• cd cleaner
• See dedicated README in the folder.

Session 3: Feature Embedder

• Tool to embed tokenized reviews into numerical vector.
• cd embedder
• See dedicated README in the folder.

Session 4: Feature Embedder with Attention Mechanism

• Tool to embed tokenized reviews into numerical vector and predict associated ratings using a Hierarchical Attention Network (HAN).
• cd attention_embedder
• See dedicated README in the folder.

Run Application from Command Line

• As seen from image below simply run the following command and set user defined parameters via GUI:

python3 launch_program.py

GUI User defined settings:

• --Save Wordcloud: option to create wordclouds per restaurants.
• --Save TFIDF: option to create TFIDF embedding per restaurants.
• --Embedding Technique: define embedding technique (lsi, word2vec, fasttext) supported.

Script to merge data from multiple scrapping runs, create a balanced dataset of reviews (ratings 1-5), clean selected reviews and embed words into vectors depending on user defined embedding technique (lsi, word2vec, fasttext and all are supported).

Contributors

Project realized by @elalamik, @erraya, @hehlinge42, @louistransfer and @MaximeRedstone