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The labeled dataset consists of 50,000 IMDB movie reviews. No individual movie has more than 30 reviews. The 25,000 reviews labeled training set does not include any of the same movies as the 25,000 review test set.
This project aims to perform sentiment analysis on the IMDB movie review dataset. It utilizes deep learning techniques, particularly LSTM and Conv1D layers, to classify movie reviews into positive and negative sentiments. The model is built using Keras and GloVe embeddings for word representations.
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.
In this project, I will use IMDB movie reviews. This dataset contains 50,000 movie's reviews from IMDB, labeled by sentiment (positive/negative). The dataset can be loaded and splitted into training and test sets as the following.
There are 25,000 highly polar movie reviews for training, and 25,000 for testing. We use Hugging Face's awesome datasets library to get the pre-processed version of the original IMDB dataset.
9 lis 2022 · This is a practice notebook to work with a dataset of 50,000 movie reviews from the Internet Movie Database (IMDB) and build an LSTM predictor to distinguish between positive and negative reviews.
In this case study, our objective is to classify movie reviews as positive or negative. This is a classic binary classification, which aims to predict one of two classes (positive vs. negative). To predict whether a review is positive or negative, we will use the text of the movie review. ℹ️.
