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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.
Overview. This project implements sentiment analysis in natural language processing (NLP) using machine learning techniques. The goal is to classify movie reviews as positive or negative based on the sentiment expressed in the text. Features. Preprocesses raw text data to remove noise and standardize the format.
Large Movie Review Dataset. Large Movie Review Dataset. code. New Notebook. table_chart. New Dataset. tenancy. New Model. emoji_events. New Competition. corporate_fare. New Organization. No Active Events. Create notebooks and keep track of their status here. add New Notebook. auto_awesome_motion. 0 Active Events. expand_more ...
Explore and run machine learning code with Kaggle Notebooks | Using data from IMDB Dataset of 50K Movie Reviews.
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.
The dataset used is a balanced collection of (50,000 - 1:1 train-test ratio) IMDB movie reviews with binary labels: postive or negative from the paper by Maas et al. (2011). The current...
This project focuses on sentiment analysis of movie reviews using the IMDb dataset. The dataset consists of 50,000 movie reviews labeled as positive or negative. The main goal of this project is to develop models that can accurately classify the sentiment of movie reviews.
