https://github.com/isabelleysseric/sentiment-analysis

Sentiment analysis with dependency tree.
https://github.com/isabelleysseric/sentiment-analysis

bag-of-words-model corpus dependency-analysis dependency-parsing dependency-tree dependency-trees displacy embedding multigram nlp nltk parsing pos-tagging scope-of-negation sentiment-analysis sentimental-analysis sentiwordnet spacy text-classification

Last synced: about 2 months ago
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Sentiment analysis with dependency tree.

• Host: GitHub
• URL: https://github.com/isabelleysseric/sentiment-analysis
• Owner: isabelleysseric
• Created: 2022-09-30T17:33:22.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-09-10T23:16:33.000Z (4 months ago)
• Last Synced: 2024-09-11T03:46:01.415Z (4 months ago)
• Topics: bag-of-words-model, corpus, dependency-analysis, dependency-parsing, dependency-tree, dependency-trees, displacy, embedding, multigram, nlp, nltk, parsing, pos-tagging, scope-of-negation, sentiment-analysis, sentimental-analysis, sentiwordnet, spacy, text-classification
• Language: Python
• Homepage:
• Size: 8.89 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
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## 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