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https://github.com/adarshkoppmanjunath/sentimentalanalysis
Most commonly used algorithms was tried for sentiment analysis of the IMDb dataset. From the experiment, it was observed that the highest accuracy is from SVM, followed by Blend, Logistic Regression and Naive Bayes. In all the four classifying techniques, it was observed that Tf-Idf with n-grams pre-processing technique seemed to yield better performances across all. Hence, it can be stated that for the chosen dataset in the split of 80-20, tf-idf seemed better than count vectorization.
https://github.com/adarshkoppmanjunath/sentimentalanalysis
python sentiment-analysis text-analysis
Last synced: 3 days ago
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Most commonly used algorithms was tried for sentiment analysis of the IMDb dataset. From the experiment, it was observed that the highest accuracy is from SVM, followed by Blend, Logistic Regression and Naive Bayes. In all the four classifying techniques, it was observed that Tf-Idf with n-grams pre-processing technique seemed to yield better performances across all. Hence, it can be stated that for the chosen dataset in the split of 80-20, tf-idf seemed better than count vectorization.
- Host: GitHub
- URL: https://github.com/adarshkoppmanjunath/sentimentalanalysis
- Owner: AdarshKoppManjunath
- Created: 2020-01-14T03:18:54.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-01-14T03:20:10.000Z (almost 5 years ago)
- Last Synced: 2023-03-04T13:07:58.591Z (over 1 year ago)
- Topics: python, sentiment-analysis, text-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 11.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0