Movie recommendation based on emotion in Python
Introduction One of the underlying targets of movies is to evoke emotions in their viewers. IMDb offers all the movies for all genre. Therefore the movie titles can be scraped from the IMDb list to recommend to the user.IMDb does not have an API, for accessing information on movies and TV Series. Therefore we have to perform scraping. Scraping is used for accessing information from a website which is usually done with APIs.
Install BeautifulSoup and lxml,
Open terminal and write
pip install beautifulsoup4 pip install lxml
The scraper is written in Python and uses lxml for parsing the webpages. BeautifulSoup is used for pulling data out of HTML and XML files.
Emotion associated with Genre of Movie
There are 8 classes of emotion that would be effective to classify a text. These are: ‘Anger’, ‘Anticipation’, ‘Disgust’, ‘Fear’, ‘Joy’, ‘Sad’, ‘Surprise’, ‘Trust’. Here these are taken as input and the corresponding movies would be displayed for the emotion.
The correspondence of every emotion with genre of movies is listed below:
Sad – Drama
Disgust – Musical
Anger – Family
Anticipation – Thriller
Fear – Sport
Enjoyment – Thriller
Trust – Western
Surprise – Film-Noir
Based on the input emotion, the corresponding genre would be selected and all the top 5 movies of that genre would be recommended to the user.
This script would scrape all the movie titles of the genre corresponding to the input emotion and list to the user.
Web Scraping is highly beneficial in extracting the data and doing analysis on it. Without web scraping, the Internet as you know it really wouldn’t exist. That’s because Google and other major search engines rely upon a sophisticated web scraper to pull the content that will get included in their index. These tools are what makes search engines possible.
Applications of Crawling
- Article extraction for websites that curate content.
- Business listings extraction for companies that build databases of leads.
- Many different types of data extraction, sometimes called data mining. For example, one popular and sometimes controversial use of a web scraper is for pulling prices off of airlines to publish on airfare comparison sites.