Advanced Web Scraping: Extracting Detailed Movie Information from IMDB

Web scraping continues to be a powerful technique for data extraction from websites. In this comprehensive guide, we’ll explore how to extract detailed movie information from IMDB by leveraging HTML and JSON data structures.

Building on Previous Work

Previously, we scraped basic information from IMDB’s top 250 movies page, including movie titles, thumbnails, release years, durations, ratings, and movie IDs. This foundation provides us with the essential movie IDs needed to access individual movie pages for more detailed information.

Accessing Individual Movie Pages

Rather than connecting to a table as in our previous scraping effort, we’ll now connect directly to the HTML code of individual movie pages. This approach gives us access to rich information such as:

Detailed descriptions
High-resolution movie posters
Exact number of votes (not just the abbreviated format)
Directors
Genre information
Keywords
Content ratings
Precise duration in minutes

Working with JSON Data

While inspecting the page source, we discover that much of the valuable information is stored in a JSON format embedded within the HTML. This structured data makes extraction more reliable than parsing HTML directly.

The process involves:

Extracting the JSON data segment from the HTML
Converting this text to a proper JSON structure
Converting the JSON into a table format for easier manipulation
Transposing and organizing the data

Creating Dynamic Connections

To scale our scraping to multiple movies, we need to create a dynamic connection process. We use the movie IDs collected previously to construct URLs following the pattern of IMDB’s movie pages (https://www.imdb.com/title/[MOVIE_ID]).

Using web.contents() function allows us to refresh queries in the service, rather than creating dynamic data sources which aren’t supported.

Extracting Specific Data Points

After successfully connecting to the movie pages and extracting the JSON data, we can create custom columns for specific information:

Movie descriptions
High-resolution poster URLs
Exact rating counts (votes)
Genres (which may include multiple values per movie)
Directors

Handling Inconsistencies

Not all movies contain the same data fields. For example, some movies might not have a ‘datePublished’ field. Our scraping solution needs to account for these inconsistencies to avoid errors when processing large numbers of movie pages.

Final Processing

After extracting all desired information, we organize the data into a clean format, removing any filters applied during development, and prepare it for loading into our data model.

Conclusion

Advanced web scraping often requires a combination of techniques to extract comprehensive data. By leveraging embedded JSON data within HTML pages, we can obtain detailed movie information that would be difficult to extract through standard HTML parsing alone.

This approach allows for more accurate and complete data extraction, providing richer datasets for analysis, recommendation engines, or other data-driven applications.