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.

本Python笔记本（研究工作）提供了一套完备的文本分析与提示提取解决方案，可用于基于输入文本构建计算场景，具备广泛实用价值。
该笔记本集成了一系列功能函数，可实现文本数据预处理，以及从文本中提取角色、关系、情感、日期、时间、地址、地点、核心意图与提示信息。
核心功能：
1. 数据预处理：本笔记本提供数据预处理能力，可移除冗余字符串、标准化文本数据，为后续分析工作做好准备。
2. 角色提取：内置函数可从文本中提取角色信息，统计角色总数，并区分男性与女性角色的数量。
3. 关系提取：提供相关函数，可计算角色间的潜在关联并提取关系名称。
4. 主导情感提取：内置函数可从文本中提取主导情感。
5. 日期与时间提取：支持从文本中提取日期与时间信息，可处理“之前”“之后”“上午”“傍晚”等时间状语表述。
6. 地址与地点提取：提供函数以从文本中提取地址与地点信息，可识别办公室、住宅、房间、浴室等特定场所。
7. 核心意图提取：内置函数可提取文本的核心意图。
8. 提示收集：支持基于特定关键词或短语，从文本中收集提示信息。
示例实现：为每个函数提供了示例Python代码，展示其高效使用方式。
本笔记本是文本分析任务的宝贵工具，可助力从文本数据中提取关键信息与提示信息，可应用于自然语言处理（Natural Language Processing，NLP）、情感分析（Sentiment Analysis）、信息检索（Information Retrieval）等多个领域。其代码文档完善，可轻松集成至现有项目或工作流程中。