Sentiment Analysis for E-Commerce

Team

• Isabelle Eysseric
• Nicolas Garde
• David Poisson

Scenario

PyCharm with Python 3.7 was used so that the Spacy library could work properly.

We wrote 2 methods to convert the sentences: one that writes the dataset with children/head (1h) and the other without (20 minutes).

In agreement with Professor Luc Lamontagne, to make task 3 go faster, we wrote the converted sentences of the method using the dependency tree of the negation_conversion.py file in text files.

These files were produced with the write_negated method found in the sentiment_analysis.py file. They are stored in the data folder.

Also, it is necessary to download the NLTK corpus, Sentiwordnet so that the sentiment_analyse.py file can work.

Steps

( See file negation_conversion.py )

• Step 1: Find the scope of the sentence
• Step 2: Capture the negative scope
• Step 3: Conversion and reconstruction of the sentence