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Explore and run machine learning code with Kaggle Notebooks | Using data from IMDB Dataset of 50K Movie Reviews.
Mar 3, 2024 · This research paper presents a comprehensive comparison of traditional machine learning techniques and advanced transformer-based models for IMDb movie reviews sentiment analysis.
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.
The report proposes a methodology to conduct the sentiment analysis of IMDb reviews. The methodology has three major steps, as shown in Fig. 1. As the results show, the binary and 3 grams vectorization performs best among all three vectorizations for all the algorithms.
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.
This paper aims to use an IMDB database that contains 50,000 reviews, and we intend to apply transformer-based language models like Bidirectional Encoder Representations from Transformers (BERT), RoBERTa, and XLNet for sentiment analysis.
People also ask
Where can I find sentiment analysis of IMDb movie reviews?
What language models can be used for sentiment analysis in IMDb?
How to classify sentimental representations of Internet Movie Database (IMDB) reviews?
What is IMDb movie reviews dataset?
In this project, I will use IMDB movie reviews. This dataset contains 50,000 movie's reviews from IMDB, labeled by sentiment (positive/negative). The dataset can be loaded and splitted into training and test sets as the following.
