Sentiment-Analysis-for-IMDB-Movie-Reviews

link: https://abdelrahmanragab38-sentiment-analysis-sentiment-analysis-dkw0ki.streamlit.app/

About

The dataset contains a collection of 50,000 reviews from the IMDB Website with an equal number of positive and negative reviews. The task is to predict the polarity (positive or negative) of a given review(text).

in this project i applied a lot of concepts

1. Loading and exploration of Data

2. Data preprocessing

 1-removing html tags
 2-taking only words 
 3-lowercase
 4-tokenization
 5-stop_words removal
 6-lemmatization

3. Vectorizing Text(reviews)

Splitting the data set into train and test(70–30) BOW (Bag Of Words) TF-IDF

4. Building ML Classifiers

1-Naive Bayes with reviews BOW encoded

2-Naive Bayes with reviews TF-IDF encoded

3-Logistic Regression with reviews TF-IDF encoded (apply L1 regulariztion)

4-Logistic Regression with reviews TF-IDF encoded (apply L2 regulariztion)

5. Summary & comparing the models

6. Save the Model dumping using pickle

7. Model deployment Using Streamlit

 link: https://abdelrahmanragab38-sentiment-analysis-sentiment-analysis-dkw0ki.streamlit.app/

8. Deep learning part using ANN to predict the sentiment

 1- I used the Bow , count , freq , tf-idf vectorizers with the ANN

9. I used the Glove word embedding , LSTM

AbdelrahmanRagab38/Sentiment-Analysis-for-IMDB-Movie-Reviews