IMDB-sentiment-classifier-using-TF-IDF-Machine-learning-using-Python

Date:Apr13 -2025

This project focuses on Imdb movie reviews using NLP.

• Dataset collection: Loaded "imdb" dataset from huggingface, it containing movie reviews text with sentiment labels 0-Negative,1-Positive.

• Text Preprocessing: Extract review texts and used nltk for cleaning. Nltk source to download it stopwords,wordnet,omw for lemmatization. Cleaned texts apply in new column.

• Data split: split dataset into train and validation sets.

• Features extraction: Clean words used tfidf to convert into numerical features.

• Model trainning: using classification algorithms logistic regression,naive bayes(multinomalNB) to train a models.

• Evaluation: validation accuracy,test accuracy for the model performance and comparision. Also did classification report precision FP,recall FN,f1 score. Logistic regression achieve the validation accuracy 88% and test accuracy 87%. Logistic regression classifier high precision,recall ,f1 score fewer of FP,FN then perform well.

• Final use: Trained model to new_user reviews input to classify as Positive or Negative. Deployed model with streamlit web UI for clear visual classification.