Chronological Narrative Reconstructor

Inspired by the fragmented, reverse-chronological narrative of 'Memento' and the structured, time-series data found in e-commerce pricing, this project aims to develop a Natural Language Processing tool capable of reconstructing disordered textual narratives. Users will input blocks of text that are out of chronological order (e.g., diary entries, news snippets, character dialogues from a fictional story). The NLP model, trained on sequential data patterns and temporal cues, will analyze these fragments, identify temporal relationships (using named entity recognition for dates/times, verb tense analysis, and semantic causality), and reassemble them into a coherent, chronological timeline. Think of it as a digital archivist for fractured stories. The niche lies in its application for writers struggling with non-linear plot development, historians piecing together events from disparate sources, or even for personal journaling where entries are made at various times. The low cost comes from utilizing open-source NLP libraries (like SpaCy or NLTK) and readily available cloud computing resources for training. The high earning potential stems from offering this as a premium API service for content creators, historical researchers, and even in the gaming industry for procedural narrative generation or reconstructing in-game lore.