Large Movie Review Dataset. 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. There is additional unlabeled data for use as well.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imdb_reviews', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

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. A negative review has a score ≤ 4 out of 10, and a positive review has a score ≥ 7 out of 10. No more than 30 reviews are included per movie. The dataset contains additional unlabeled data.

R

IMDB-MULTI is a relational dataset that consists of a network of 1000 actors or actresses who played roles in movies in IMDB. A node represents an actor or actress, and an edge connects two nodes when they appear in the same movie. In IMDB-MULTI, the edges are collected from three different genres: Comedy, Romance and Sci-Fi.

Source Huggingface Hub: link

About this dataset This is a large dataset for binary sentiment classification containing a substantial amount of data compared to previous benchmark datasets. Provided are 25,000 highly polar movie reviews for training and 25,000 for testing. There is also additional unlabeled data available for use. The data fields are consistent among all splits of the dataset

How to use the dataset In order to use this dataset, you will need to first download the IMDB Large Movie Review Dataset. Once you have downloaded the dataset, you can either use it in its original form or split it into training and testing sets. To split the dataset, you will need to create a new file called unsupervised.csv and copy the text column from train.csv into it. You can then split unsupervised.csv into two files: train_unsupervised.csv and test_unsupervised.csv.

Once you have either the original dataset or the training and testing sets, you can begin using them for binary sentiment classification. In order to do this, you will need to use a machine learning algorithm that is capable of performing binary classification, such as logistic regression or support vector machines. Once you have trained your model on the training set, you can then evaluate its performance on the test set by predicting the labels of the reviews in test_unsupervised.csv

Research Ideas This dataset can be used to train a binary sentiment classification model. This dataset can be used to train a model to classify movie reviews into positive and negative sentiment categories. This dataset can be used to build a large movie review database for research purposes

License

CC0

Original Data Source: IMDB Movie Reviews (Binary Sentiment)

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Academy Awards, also known as Oscars, are awarded every year to artists and technicians in the American and international film industry by the Academy of Motion Picture Arts and Sciences. The winners are selected out of nominees selected by the members of the academy on the basis of their votes. The award was first presented for the year 1927-28. The awards are given across over 24 categories. This dataset contains details of nominations and winners, along with the movie for which they were nominated/won since the introduction of the Oscar awards.

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