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An optimized stopwords list for sentiment analysis that retains key negations like 'not', 'won't', and 'wouldn't', along with intensity modifiers like 'very' and 'most' to preserve sentiment accuracy.
This package provides an optimized stopwords list for sentiment analysis by preserving sentiment-related words that NLTK’s default list would typically remove. By retaining key negations and intensity modifiers, this package ensures that sentiment expressions remain intact, leading to more accurate sentiment classification.
not
, won't
, wouldn't
, shouldn't
, etc.)very
, most
, more
, etc.) for better sentiment retentionpip install revised-stopwords
from revised_stopwords import get_revised_stopwords
# Get the optimized stopwords list
stopwords_list = get_revised_stopwords()
print(stopwords_list) # Output: A set of refined stopwords
from revised_stopwords import get_revised_stopwords
text = "I don't think this is a very good idea"
tokens = text.lower().split() # Basic word split
filtered_tokens = [word for word in tokens if word not in get_revised_stopwords()]
print(filtered_tokens)
# Output: ["don't", 'think', 'very', 'good', 'idea']
LookupError: Resource stopwords not found?
Please use the NLTK Downloader to obtain the resource. Run this command once to manually download stopwords
import nltk
nltk.download('stopwords')
ModuleNotFoundError: No module named 'nltk'?
This error means that the nltk
library is not installed in your environment.
Ensure NLTK is installed:
pip install nltk
Want to improve this package? Feel free to fork the repo, submit PRs, or suggest enhancements!
Fork this repository to your GitHub account.
Clone your forked repo:
git clone https://github.com/priyaa279/revised_stopwords.git
Create a new branch for your changes:
git checkout -b feature-branch-name
Make your changes and commit them:
git commit -m "Describe your change"
Push your branch to GitHub:
git push origin feature-branch-name
Submit a Pull Request (PR) for review. For major changes, please open an issue first to discuss what you'd like to modify.
This project is open-source and licensed under the MIT License.
FAQs
An optimized stopwords list for sentiment analysis that retains key negations like 'not', 'won't', and 'wouldn't', along with intensity modifiers like 'very' and 'most' to preserve sentiment accuracy.
We found that revised-stopwords demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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