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.
Installation

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.

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# Python3 code for movie
# recommendation based on
# emotion
  
# Import library for web
# scrapping
from bs4 import BeautifulSoup as SOUP
import re
import requests as HTTP
  
# Main Function for scraping
def main(emotion):
  
    # IMDb Url for Drama genre of
    # movie against emotion Sad
    if(emotion == "Sad"):
  
    # IMDb Url for Musical genre of
    # movie against emotion Disgust
    elif(emotion == "Disgust"):
  
    # IMDb Url for Family genre of
    # movie against emotion Anger
    elif(emotion == "Anger"):
  
    # IMDb Url for Thriller genre of
    # movie against emotion Anticipation
    elif(emotion == "Anticipation"):
  
    # IMDb Url for Sport genre of
    # movie against emotion Fear
    elif(emotion == "Fear"):
  
    # IMDb Url for Thriller genre of
    # movie against emotion Enjoyment
    elif(emotion == "Enjoyment"):
  
    # IMDb Url for Western genre of
    # movie against emotion Trust
    elif(emotion == "Trust"):
  
    # IMDb Url for Film_noir genre of
    # movie against emotion Surprise
    elif(emotion == "Surprise"):
  
    # HTTP request to get the data of
    # the whole page
    response = HTTP.get(urlhere)
    data = response.text
  
    # Parsing the data using
    # BeautifulSoup
    soup = SOUP(data, "lxml")
  
    # Extract movie titles from the
    # data using regex
    title = soup.find_all("a", attrs = {"href" : re.compile(r'\/title\/tt+\d*\/')})
    return title
  
# Driver Function
if __name__ == '__main__':
  
    emotion = input("Enter the emotion: ")
    a = main(emotion)
    count = 0
  
    if(emotion == "Disgust" or emotion == "Anger"
                           or emotion=="Surprise"):
  
        for i in a:
  
            # Splitting each line of the
            # IMDb data to scrape movies
            tmp = str(i).split('>;')
  
            if(len(tmp) == 3):
                print(tmp[1][:-3])
  
            if(count > 13):
                break
            count += 1
    else:
        for i in a:
            tmp = str(i).split('>')
  
            if(len(tmp) == 3):
                print(tmp[1][:-3])
  
            if(count > 11):
                break
            count+=1

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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.


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