Text Script Analytics Algorithm for Automatic Video Generation
This Python notebook (research work) provides a comprehensive solution for text analysis and hint extraction that will be useful for making computational scenes using input text . It includes a collection of functions that can be used to preprocess textual data, extract information such as characters, relationships, emotions, dates, times, addresses, locations, purposes, and hints from the text. Key Features: Preprocessing Collected Data: The notebook offers preprocessing capabilities to remove unwanted strings, normalize text data, and prepare it for further analysis. Character Extraction: The notebook includes functions to extract characters from the text, count the number of characters, and determine the number of male and female characters. Relationship Extraction: Functions are provided to calculate possible relationships among characters and extract the relationship names. Dominant Emotion Extraction: The notebook includes a function to extract the dominant emotion from the text. Date and Time Extraction: Functions are available to extract dates and times from the text, including handling phrases like "before," "after," "in the morning," and "in the evening." Address and Location Extraction: The notebook provides functions to extract addresses and locations from the text, including identifying specific places like offices, homes, rooms, or bathrooms. Purpose Extraction: Functions are included to extract the purpose of the text. Hint Collection: The notebook offers the ability to collect hints from the text based on specific keywords or phrases. Sample Implementations: Sample Python code is provided for each function, demonstrating how to use them effectively. This notebook serves as a valuable resource for text analysis tasks, assisting in extracting essential information and hints from textual data. It can be used in various domains such as natural language processing, sentiment analysis, and information retrieval. The code is well-documented and can be easily integrated into existing projects or workflows.