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Explore and run machine learning code with Kaggle Notebooks | Using data from IMDB Dataset of 50K Movie Reviews.
This project demonstrates a comprehensive approach to sentiment analysis using the IMDB movie review dataset. By leveraging deep learning techniques with Keras and GloVe word embeddings, the model classifies reviews into positive and negative sentiments.
3 Μαρ 2024 · In this paper the Long Short-Term Memory (LSTM) classifier is used for analyzing sentiments of the IMDb movie reviews. It is based on the Recurrent Neural Network (RNN) algorithm.
IMDB dataset has 50K movie reviews for natural language processing or Text analytics. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training and 25,000 for testing. So, predict the number of positive and ...
4 Μαρ 2022 · Sentiment analysis of movie reviews can help to reveal a lot of crucial data like the highs and lows of a movie and whether it managed to live up to customers’ expectations or not. Sentiment analysis is used to deconstruct user reviews for a movie down to the molecular level and then classify them as positive or negative based on the ...
Sentiment analysis is a method of determining whether a review has positive or negative sentiment, and this study investigates a machine learning method for classifying sentiment from film reviews.
Explore sentiment analysis on the IMDB movie reviews dataset using Python. This Jupyter Notebook showcases text preprocessing, TF-IDF feature extraction, and model training (Multinomial Naive Bayes, Random Forest) for sentiment classification.
