Emotion-based movie recommendations in Python

File handling | Python Methods and Functions | Regular Expressions

Install BeautifulSoup and lxml ,
Open terminal and write

 pip install beautifulsoup4 pip install lxml

The scraper is written in Python and uses lxml to parse web pages. BeautifulSoup is used to extract data from HTML and XML files.

Film-related emotions

There are 8 classes of emotions that would be efficient for classifying text ... These are: Anger, Expectation, Disgust, Fear, Joy, Sad, Surprise, Trust . Here they are taken as input and the corresponding movies will be displayed for emotion.
The correspondence of each emotion to the genre of cinema is listed below:

Garden — Drama
Disgust — Musical
Anger — Family
Waiting — Thriller
Fear — Sports
Enjoyment — Thriller
Trust — Western
Surprise — Film Noir

Based on emotions, the appropriate genre will be selected, and all 5 best films of this genre will be recommended to the user.

       # Python3 movie code  
  # recommendation based  
  # emotion  
   
  # Import web library  
  # scrapped 
   from   bs4   import   BeautifulSoup as SOUP 
   import   re   
  import   requests as HTTP 
   
  # Main function for scraping  
   def   main (emotion): 
   
     # IMDb URL for the drama genre  
    # film against emotions Garden  
    if   (emotion   =   =   "Sad"  ): 
    urlhere   =   ' http://www.imdb.com/search/title?genres=drama& title_type = feature & amp; sort = moviemeter , asc'  
    
    # IMDb URL for music genre  
    # film against emotions Disgust  
    elif   (emotion   =   =   "Disgust"  ): 
    urlhere   =   ' http://www.imdb.com/search/title?genres=musical&title_type=feature&sort = moviemeter , asc'  
   
    # IMDb URL for Family genre   
   # film against emotions Anger  
     elif   (emotion   =   =  " Anger " ): 
     urlhere   =  '  http://www.imdb.com/search/title?genres = family & amp; title_type = feature & amp; sort = moviemeter , asc'  
   
    # IMDb URL for thriller genre  
    # film against emotions  
     elif   (emotion   =   =   "Anticipation"  ): 
    urlhere   =   ' http://www.imdb.com/search/title ? genres = thriller & amp; title_type = feature & amp; sort = moviemeter , asc'  
   
    # IMDb Url for Sport genre  
     # film against fear emotions  
    elif   (emotion   =   =   "Fear"  ): 
    urlhere   =   ' http://www.imdb.com/ search / title? genres = sport & amp; title_type = feature & amp; sort = moviemeter , asc'  
   
    # IMDb URL for genre thriller  
    # film against emotions  
    elif   (emotion   =   =   "Enjoyment"  ): 
     urlhere   =  '  http://www.imdb.com/search/title?genres = thriller & amp; title_type = feature & amp; sort = moviemeter , asc'  
   
    # IMDb URL for Western genre  
    # film against emotions of trust  
    elif   (emotion   =   =   "Trust"  ): 
    urlhere   =   ' http://www.imdb.com/search/title?genres= western & amp; title_type = feature & amp; sort = moviemeter , asc'  
   
    # IMDb URL for Film_noir genre  
    # film against emotions surprise  
    elif   (emotion   =   =   "Surprise"  ): 
    urlhere   =   ' http://www.imdb.com/search/title?genres=film_noir&title_type=feature&sort=moviemeter , asc'  
   
    # HTTP request to get data  
    # whole page  
    response   =   HTTP.get (urlhere)  
    data   =   response.text 
   
     # Parse data with  
    # BeautifulSoup  
    soup   =   SOUP (data,   "lxml"   ) 
   
    # Extract movie titles from  
     # data using regular expressions  
    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: 
   
    # Separate each line  
    # IMDb data for To clean up movies  
    tmp   =   str   (i) .split (  '& gt ;;'  ) 
   
    if   (  len   (tmp)   =   =   3  ): 
    print   (tmp [  1  ] [:   -   3  ]) 
    
   if   (count & gt;   13  ): 
     break  
    count   +   =   1  
    else  : 
     for   i   in   a : 
    tmp   =   str   (i) .split (  '& gt;'  ) 
   
     if   (  len   (tmp)   =   =   3  ): 
    print   (tmp [  1  ] [:   -   3  ]) 
   
    if   (count & gt;   11  ): 
     break  
    count   +   =   1

This script will scrape all movie titles in the genre corresponding to the entered emotion and make a list for the user.

Web Scraping is very useful for extracting data and analyzing it. Without Web Cleaning, the Internet, as you know, would not really exist. This is because Google and other major search engines use a sophisticated web scraper to extract content to be included in their index. These tools make search engines possible.

Application crawling

Article retrieval for sites that curate content.
Business list retrieval for companies that create databases of leads.
Many different types of data extraction, sometimes referred to as data extraction. For example, one of the popular and sometimes controversial uses of the web scraper — This is a reduction in airline prices for publication on airfare comparison sites.

Emotion-based movie recommendations in Python

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