KPTopic

• No more Topic Modeling
• No need Training
• Text Big + Small
• Emoji + Sentiment Score

KPTopic: a graph-based approach to represent perception (text in general) by key parts of speech. KPTopic solved the coherence crux that current topic modeling algorithms are trying to deal with but failed. KPTopic extracts the topics from text corpus syntactically, semantically and pragmatically instead of a meaningless combination of words from topic modeling.

Key Parts: Noun, Adjective, Verb and Emoji

KPTopic Vs Topic Modeling results from the following text:

“Thai food was great we loved it. Thiland also has beautiful beach resorts, we will come to Thailand again👍”

• KPTopic Result

• Topic Modeling Result

['food','thailand','resort','great','love', 'beautiful']

Installation

 # pip install kptopic
 # pip install git+https://github.com/Atsaniik/kptopic.git  

Getting Started

For an in-depth overview of the features of KPTopic you can check the Documents or you can follow along with one of the examples as follows:

Name	Link
KPTopic Quick Start

Visualization Examples

• 1 NLP Target

Original sentence: """Thai food was great,delicousr and not expensive, we loved it. We visited 3 beach resorts, they are higly recommened... We had "Fire-Vodka" !!!"""

• 2 Keyparts Wordclouds

The following wordclouds are generated from a real example of corpus comprised of reviews by those who visit Thailand.

• 3 Community and Gray Perceptual Unit Networks

Citation

To cite the KPTopic paper, please use the following bibtex reference:

@article{pengyang,
  title={KPTopic},
  author={Peng, Yang},
  journal={a1},
  year={202}
}