MovieRecommendationSystem_-_SentimentAnalysis

Content based Movie-Recommendation-System with Sentiment-Analysis on User review

Overview

This project involves building a Movie Recommendation System and Sentiment Analysis tool with a user interface. The recommendation system utilizes cosine similarity, while sentiment analysis employs a Naive Bayes machine learning model. The web application is built using Flask API framework.

User Interface

Shown in Image display. You can also check full video link provided below.

Technologies Used

Programming Language: Python
Libraries: Numpy, Pandas, Flask, Scikit-Learn, BeautifulSoup, Pickle, Requests, TMDb API
Machine Learning Models: Cosine Similarity, Naive Bayes
Web Scraping: BeautifulSoup for IMDb reviews
Web Framework: Flask
Frontend: HTML with Jinja templating
API: TMDb API for movie information

Components

Movie Recommendation System Preprocessing: CountVectorizer for creating a count matrix Similarity Calculation: Cosine Similarity between movies Recommendation Function: rcmd(m)

Sentiment Analysis NLP Model: Naive Bayes model loaded from 'nlp_model.pkl' Vectorizer: TF-IDF Vectorizer loaded from 'tranform.pkl' Prediction Function: Sentiment analysis performed on IMDb reviews

UI and Web Application

Flask Application: Defines routes for home page and recommendation HTML Templates: 'home.html' and 'recommend.html' for rendering pages User Input Handling: Takes user input for movie recommendation TMDb API Integration: Retrieves additional movie information and posters

Utility Functions

Genres and Date Conversion: Functions for processing genres and converting dates Time Conversion: Function for converting movie duration from minutes to hours and minutes Suggestions: Provides a list of movie suggestions

How to get the API key?

Create an account in https://www.themoviedb.org/, click on the API link from the left hand sidebar in your account settings and fill all the details to apply for API key. You will see the API key in your API sidebar once your request is approved.

How to Run the project

Install all the libraries mentioned in the requirements.txt file. Clone this repository in your local system. Replace YOUR_API_KEY in the main.py file. Open the command prompt from your project directory and run the command python main.py. Go to your browser and type http://127.0.0.1:8080 in the address bar. Hurray! That's it.