IMDb Sentiment Review Analysis Using Advanced Machine Learning Techniques for Enhanced Movie Recommendation Systems and Customer Experience

Project Abstract:

Background: Sentiment analysis of movie reviews can provide valuable insights into viewer preferences, enabling the development of more accurate recommendation systems and enhancing customer experience. The IMDb dataset, which contains a large collection of movie reviews, offers an opportunity to apply machine learning (ML) techniques for sentiment analysis, facilitating data-driven decision-making in the film industry. This project aims to develop a robust and reliable sentiment analysis model using advanced machine learning techniques, leveraging the IMDb dataset to enhance movie recommendation systems and customer experience.

Objectives:

1. To collect, preprocess, and analyze movie reviews from the IMDb dataset, focusing on sentiment polarity (positive, negative) and sentiment intensity.
2. To implement natural language processing (NLP) techniques for effective text preprocessing and feature extraction, including tokenization, stopword removal, stemming, and vectorization.
3. To develop a high-performance sentiment analysis model using various machine learning algorithms, including classification, ensemble methods, and deep learning.
4. To evaluate the performance of the sentiment analysis model using appropriate metrics and validate its effectiveness in analyzing the IMDb movie reviews.
5. To provide actionable insights and recommendations for movie recommendation systems and customer experience improvement based on the IMDb sentiment review analysis.

Methods:

1. Data collection and preprocessing: The project will involve the collection and preprocessing of movie reviews from the IMDb dataset. Text preprocessing steps, such as tokenization, stopword removal, stemming, and vectorization, will be performed to ensure the data is suitable for ML model training.
2. Feature extraction: Natural language processing techniques, such as Bag of Words, Term Frequency-Inverse Document Frequency (TF-IDF), and word embeddings (e.g., Word2Vec, GloVe), will be used to extract relevant features from the movie reviews.
3. Model development: ML algorithms, including Logistic Regression, Naive Bayes, Decision Trees, Random Forest, Support Vector Machines, and deep learning models like Neural Networks and LSTM, will be applied to develop the sentiment analysis model. Hyperparameter tuning and model selection will be conducted through cross-validation and grid search techniques.
4. Model evaluation: The performance of the ML models will be assessed using metrics such as accuracy, precision, recall, F1-score, and area under the Receiver Operating Characteristic (ROC) curve.
5. Insights and recommendations: The IMDb sentiment review analysis will be used to derive insights and recommendations for movie recommendation systems and customer experience improvement.

Expected Outcomes: The project will result in a comprehensive sentiment analysis model capable of accurately analyzing movie reviews from the IMDb dataset. The implementation of this model in movie recommendation systems will enable more accurate recommendations, leading to enhanced customer experience and data-driven decision-making in the film industry.

Keywords: IMDb dataset, sentiment analysis, movie reviews, machine learning, natural language processing, text preprocessing, feature extraction, classification, deep learning, movie recommendation systems, customer experience.