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keras.datasets.imdb.load_data( path="imdb.npz", num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3, **kwargs ) Loads the IMDB dataset. This is a dataset of 25,000 movies reviews from IMDB, labeled by sentiment (positive/negative). Reviews have been preprocessed, and each review is encoded as a list of word ...
SST. The IMDb Movie Reviews dataset is a binary sentiment analysis dataset consisting of 50,000 reviews from the Internet Movie Database (IMDb) labeled as positive or negative. The dataset contains an even number of positive and negative reviews. Only highly polarizing reviews are considered.
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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.
A comprehensive project demonstrating sentiment analysis on IMDb movie reviews using NLP and machine learning. The project covers data preprocessing, feature extraction with TF-IDF, model training with logistic regression, and performance evaluation. Ideal for showcasing skills in text data handling and sentiment classification. Resources
Explore and run machine learning code with Kaggle Notebooks | Using data from IMDB Dataset of 50K Movie Reviews.
About. IMDB dataset having 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.
Sentiment Analysis on IMDB Movie Reviews. Article | Github | More Notebooks @ eugenesiow/practical-ml. Notebook to train an XLNet model to perform sentiment analysis. The dataset used...
