Sentiment Analysis Model for Restaurant Reviews

This project implements a sentiment analysis model for evaluating restaurant reviews. The model identifies aspects such as food, drinks, and service, and extracts related sentiment expressions from the reviews.

Installation

Prerequisites

• Python 3.x
• Gensim
• SpaCy
• SciPy

Setup

1. Clone the repository:

git clone https://github.com/yourusername/restaurant-sentiment-analysis.git
cd restaurant-sentiment-analysis

2. Install required libraries:

pip install gensim spacy scipy

3. Download the SpaCy French model:

python -m spacy download fr_core_news_md

4. Download the word embeddings file frWac_non_lem_no_postag_no_phrase_200_skip_cut100.bin and place it in the project directory.

Usage

Run the analysis on a text file with reviews:

python sentiment_analysis.py reviews.txt

Results are saved in resultats.json.

Files

• sentiment_analysis.py: Main script.
• frWac_non_lem_no_postag_no_phrase_200_skip_cut100.bin: Word embeddings (not included).
• reviews.txt: Input file.
• resultats.json: Output file.

Functionality

• Loads word embeddings and SpaCy model.
• Defines aspect keywords for food, drinks, and service.
• Calculates similarity between words and aspect keywords.
• Identifies aspect terms and associated sentiments in reviews.
• Saves results in JSON format.