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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.\n\n## Project Goal\n\nFew of our main goals are:\n\n1. **Data Preprocessing**: Clean and preprocess the IMDb movie review dataset, including handling missing values, text cleaning, and tokenization.\n\n2. **Exploratory Data Analysis (EDA)**: Perform EDA to gain insights into the data distribution, class balance, and other characteristics of the dataset.\n\n3. **Sentiment Analysis Model**: Develop a sentiment analysis model that can classify movie reviews as positive or negative based on their text content.\n\n4. **F1 Score of 0.85**: Achieve a minimum F1 score of 0.85 to ensure the model's accuracy in detecting negative reviews.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F5hraddha%2Fsentiment-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F5hraddha%2Fsentiment-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F5hraddha%2Fsentiment-analysis/lists"}