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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.
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.
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 indexes (integers).
Dataset of 25,000 movies reviews from IMDB, labeled by sentiment (positive/negative). Reviews have been preprocessed, and each review is encoded as a sequence of word indexes (integers).
This project is focused on performing sentiment analysis on a dataset of IMDb movie reviews. The goal is to classify reviews as positive or negative based on the textual content. Three different models are planned to compare their performance on this task: Naive Bayes Classifier ☑; Logistic Regression ☑; Long Short-Term Memory Networks ...
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.
IMDB movie review sentiment classification dataset. Description. The format of this dataset is meant to replicate that provided by Keras . Usage. imdb_dataset( root, download = FALSE, split = "train", shuffle = (split == "train"), num_words = Inf, skip_top = 0, maxlen = Inf, start_char = 2, oov_char = 3, index_from = 4. ) Arguments.
