In the age of digital content and streaming services, movie reviews play a pivotal role in helping viewers make informed choices about what to watch. The "Film Junky Union" project, an exciting venture for classic movie enthusiasts, seeks to revolutionize this experience by developing an innovative system for filtering and categorizing movie reviews. By leveraging the power of machine learning, our primary objective is to create a model capable of automatically detecting negative movie reviews, aiding film aficionados in avoiding cinematic disappointments.To achieve this, we will utilize a comprehensive dataset of IMDb movie reviews, with polarity labels indicating whether a review is positive or negative. Through data preprocessing, exploratory data analysis (EDA), model training, and rigorous testing, we aim to construct a robust classifier capable of achieving an F1 score of at least 0.85. The project's findings will not only provide valuable insights into sentiment analysis within the film industry but also empower movie enthusiasts to make more informed viewing decisions.
Few of our main goals are:
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Data Preprocessing: Clean and preprocess the IMDb movie review dataset, including handling missing values, text cleaning, and tokenization.
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Exploratory Data Analysis (EDA): Perform EDA to gain insights into the data distribution, class balance, and other characteristics of the dataset.
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Sentiment Analysis Model: Develop a sentiment analysis model that can classify movie reviews as positive or negative based on their text content.
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F1 Score of 0.85: Achieve a minimum F1 score of 0.85 to ensure the model's accuracy in detecting negative reviews.