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12 sie 2024 · 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.
3 mar 2024 · In this paper the Long Short-Term Memory (LSTM) classifier is used for analyzing sentiments of the IMDb movie reviews. It is based on the Recurrent Neural Network (RNN) algorithm.
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.
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.
25 mar 2022 · PDF | On Mar 25, 2022, Ayanabha Ghosh published Sentiment Analysis of IMDb Movie Reviews : A comparative study on Performance of Hyperparameter-tuned Classification Algorithms | Find,...
31 maj 2019 · This paper presents a comparative investigation of different techniques used for sentiment analysis in product reviews to discover which AI-based technique works pre-eminent for review...
This dataset consists of 50,000 movie reviews taken from IMDb. Half of the data is used for training while the other half is used for testing. Moreover, both the training and testing dataset have 50% of positive reviews and 50% of negative reviews. In each of the reviews, users are allowed to rate the movie from 1 to 10.
