Chronological Text Unscrambler

Imagine a scenario where you have a collection of text documents – perhaps a lost author's notes, ancient scrolls pieced together, or leaked chat logs – but the original order is unknown. Like Leonard Shelby in 'Memento', users are presented with fragments and must piece together the narrative. Like 'Dune' uses layered storytelling that reveals information out of chronological order, this project will utilize NLP techniques to unravel complex narratives. The core concept is to build a Natural Language Processing tool that analyzes the content of these text fragments, identifying subtle temporal cues (e.g., verb tense, topic evolution, named entity relationships, sentiment shifts) to infer the most likely chronological order. This 'Chronological Text Unscrambler' will be built in stages. First, a simple version using sentence similarity and pronoun resolution to connect adjacent fragments will be created. Then, more advanced features such as topic modeling to track the evolution of themes, sentiment analysis to detect emotional arcs, and named entity recognition to identify individuals and their changing roles will be implemented. The project will leverage pre-trained language models (e.g., BERT, RoBERTa) for feature extraction and a rule-based or machine learning approach to rank potential chronological orderings. Users would upload their text fragments, and the tool would output the most probable timeline, along with a confidence score for each fragment's placement. The earning potential lies in offering this as a service to researchers, historians, journalists, and content creators. Potential applications include reconstructing historical events from fragmented documents, analyzing leaked data dumps to understand unfolding events, or helping authors build non-linear narratives like in 'Dune' with narrative threads properly identified. The niche is the application of NLP to explicitly reconstruct chronological order in ambiguous or intentionally fragmented datasets, going beyond simple text summarization or topic extraction. The low cost is achieved by using free or open-source NLP libraries and pre-trained models.