Apache OpenNLP

Apache OpenNLP Sandbox

HumanNameParser.java, a Java port of HumanNameParser.php. Parser for human names in Java, all credit goes to @jasonpriem

a domain-specific language for text substitution

An application which takes in live speech or audio recording as input, converts it into text and displays the relevant Indian Sign Language images or GIFs.

Texel is a converting tool between text and colors. All symbols define hexadecimal values, convert to RGB values and set pixels.

A project that harnesses the Stanford NLP library to gauge sentiment from provided text via an intuitive graphical interface.

A Comprehensive Toolbox for Mastery in String Operations Across Programming Paradigms 🚀🔍

Apache OpenNLP Models

MS Word as a LaTeX editor/previewer

Mini project for NTU-SC1015 (Introduction to Data Science and Artificial Intelligence). Regarding fake news analysis & classification

LINE Chat History Parser for Python

This application fixes the issue of missing lyrics on Spotify. It fetches them from other lyrics providers rather than the ones Spotify is in partnership with.

Text Preprocessing with NLTK

TerminalDesigner is a Python-based project aimed at enhancing text processing capabilities in the terminal. It provides a set of tools and functionalities to manipulate text appearance, create ASCII art, and modify terminal colors

"Detect sarcasm effortlessly! This Python app uses NLP and ML to analyze text sentiment, distinguishing sarcastic tones. With a user-friendly interface, input any text for real-time sarcasm identification. Achieve accurate results through advanced sentiment analysis techniques and trained models."

The author implemented simple rule base solution and machine learning approach for information retrieval and information extraction after which the result were analyzed.

Web & social media scraping in Pythonian way

The author implemented support vector machine for sentiments analysis and applied two feature extractions, Bag-of-Words (CountVectorizer) and TF-IDF (TfidfVectorizer), after which the results for both methods were analysed. The accuracy obtained for both methods were (BoW = 87%) and (TF-IDF = 86%).