https://github.com/rpdevjesco/morenlpfun

No dependencies on other frameworks.
https://github.com/rpdevjesco/morenlpfun

Last synced: 14 days ago
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No dependencies on other frameworks.

• Host: GitHub
• URL: https://github.com/rpdevjesco/morenlpfun
• Owner: RPDevJesco
• Created: 2024-06-12T19:46:26.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-12T19:48:47.000Z (5 months ago)
• Last Synced: 2024-06-14T02:07:41.384Z (5 months ago)
• Language: C#
• Size: 8.43 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# MoreNLPFun

This project involves the creation and manipulation of a language model capable of generating and analyzing sentences. It leverages an array of programming techniques and data processing to achieve sophisticated text manipulation and language analysis functionalities.

Functionality

Data Acquisition and Parsing

The application begins by downloading JSON-formatted dictionary data from specified URLs. This data is then deserialized into a structured format that allows for further manipulation. The main objective in this phase is to populate a dictionary with words and their associated data, which includes definitions, parts of speech, and examples.

Text Preprocessing

Once the data is loaded, it undergoes preprocessing to organize and simplify the subsequent operations. Words are standardized to lowercase to ensure uniformity and are stored in a way that groups all data related to a single word together, enhancing the efficiency of future searches.

Sentence Training and Generation

The core of the project involves training a sentence construction model using a variety of text sources. This model learns the structure and sequence of words in English sentences and is then used to generate coherent sentences up to a specified maximum length. The generated sentences are constructed using learned sequences, ensuring they mimic the style and coherence of human-written text.

Sentiment Analysis

In addition to generating sentences, the program also analyzes the sentiment of constructed sentences. It evaluates the positive or negative connotations of words based on their definitions in the loaded dictionary. The sentiment score is computed as an average of individual word sentiments, providing a quantitative measure of the sentence's overall sentiment.

Results Display

Finally, the application displays the generated sentences and their sentiment analysis results, showcasing the capabilities of the implemented models and data processing techniques.

Summary

This project exemplifies the integration of various computational techniques to achieve complex language processing tasks. It combines data acquisition, natural language processing, machine learning, and sentiment analysis to create a versatile tool capable of understanding and generating natural language. The implementation highlights the potential of combining structured data with algorithmic learning to enhance and automate text manipulation and analysis.