The TV Show Search Platform is a powerful tool designed to facilitate the search for TV shows based on keywords. Leveraging the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm, this platform extracts relevant information from TV show subtitles, making it easier for users to find shows matching their interests.
-
Keyword Search: Users can input keywords to find TV shows related to specific topics or themes.
-
TF-IDF Algorithm: The platform utilizes the TF-IDF algorithm to calculate the importance of keywords within the TV show subtitles. This allows for efficient and accurate matching.
-
Subtitle Analysis: The platform extracts and analyzes words from TV show subtitles to enhance the search functionality.
-
Keyword Input: Users enter keywords related to the TV shows they are looking for.
-
TF-IDF Matching: The platform applies the TF-IDF algorithm to identify TV shows that best match the provided keywords. This ensures that shows with a higher relevance to the input keywords are ranked higher in the search results.
-
Subtitle Extraction: The platform extracts and analyzes words from TV show subtitles to strengthen the matching process. This enables a more comprehensive understanding of the show's content.
-
Search Results: Users receive a list of TV shows ranked according to their relevance to the input keywords. This makes it easier for users to discover shows that align with their interests.
Building the TV Show Search Platform involved tackling substantial amounts of data and extracting relevant information efficiently. Here are some key learnings from the project:
-
Data Cleaning with Python: I gained proficiency in using Python for cleaning and processing large datasets. This involved removing irrelevant information and extracting key details needed for the TF-IDF algorithm.
-
TF-IDF Implementation: I learned how to implement the TF-IDF algorithm to calculate the importance of keywords within a collection of TV show subtitles. This algorithm played a crucial role in determining the relevance of shows to user-inputted keywords.
-
Subtitle Analysis Techniques: Extracting meaningful information from TV show subtitles required the development of effective analysis techniques. This involved understanding natural language processing concepts to enhance the accuracy of keyword matching.
-
Efficient Data Extraction: Dealing with a large amount of data required the development of efficient data extraction processes. This involved selecting and extracting relevant information to optimize the search functionality.
Contributions are welcome! If you have suggestions, bug reports, or feature requests, please open an issue on the GitHub repository.