https://github.com/datarohit/imdb-reviews-sentiment-analysis
This is a NLP - Sentiment Analysis Project built using Bernoulli-Naive-Bayes Algorithm to Predict is the IMDB Movie Review is Positive or Negative.
https://github.com/datarohit/imdb-reviews-sentiment-analysis
bernoulli-naive-bayes classification count-vectorizer multinomial-naive-bayes nltk numpy pandas porter-stemmer regex sentiment-analysis sklearn
Last synced: about 2 months ago
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This is a NLP - Sentiment Analysis Project built using Bernoulli-Naive-Bayes Algorithm to Predict is the IMDB Movie Review is Positive or Negative.
- Host: GitHub
- URL: https://github.com/datarohit/imdb-reviews-sentiment-analysis
- Owner: DataRohit
- Created: 2022-06-16T12:09:32.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2022-06-16T13:25:04.000Z (about 4 years ago)
- Last Synced: 2025-02-15T07:47:37.898Z (over 1 year ago)
- Topics: bernoulli-naive-bayes, classification, count-vectorizer, multinomial-naive-bayes, nltk, numpy, pandas, porter-stemmer, regex, sentiment-analysis, sklearn
- Homepage: https://www.kaggle.com/code/datarohitingole/movie-review-sentiment-analysis-naivebayes
- Size: 158 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: Readme.md
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README
# IMDB Reviews - Sentiment Analysis
## Data
#### Data for this project is taken from Kaggle. It contains 50K Movie Reviews categorised into 'positive' and 'neagtive'. The review text is uncleaned and needs preprocessing. Find this data [here](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews).
## Files
#### The code is written in python using google colab. Find all the for the project [here](https://drive.google.com/drive/folders/1dS0wjjAfZgmgvPmwJ-FkEfr_yYu3-A1H?usp=sharing).
## Analysis
#### The analysis on this data is performed using Bernoulli Naive Bayes giving 87-88% accuracy and Multinomial Naive Bayes giving 88-89% accuracy.
## End-to-End
#### The webapp to test this sentiment analysis model is built using streamlit. View streamlit docs [here](https://docs.streamlit.io/). Below is the image of the webapp desing.
