Documentation and scripts of khasi-khasi Dictionary Digitalisation project
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Updated
Sep 3, 2021 - Python
Documentation and scripts of khasi-khasi Dictionary Digitalisation project
Some example code I've written during my PhD in Applied Linguistics and Technology.
Mini project for NTU-SC1015 (Introduction to Data Science and Artificial Intelligence). Regarding fake news analysis & classification
HumanNameParser.java, a Java port of HumanNameParser.php. Parser for human names in Java, all credit goes to @jasonpriem
Texel is a converting tool between text and colors. All symbols define hexadecimal values, convert to RGB values and set pixels.
This assignments focuses on implementing and applying various AI algorithms and techniques.
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
A project that harnesses the Stanford NLP library to gauge sentiment from provided text via an intuitive graphical interface.
Web & social media scraping in Pythonian way
a domain-specific language for text substitution
"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."
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.
A Comprehensive Toolbox for Mastery in String Operations Across Programming Paradigms 🚀🔍
This project represents my team's contribution to the semi-final of Gelar Rasa 2023, a competition organized by HIMASADA UPN "Veteran" East Java. With enthusiasm and dedication, our team managed to secure the 2nd place in the competition.
Text Preprocessing with NLTK
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%).
The author implemented simple rule base solution and machine learning approach for information retrieval and information extraction after which the result were analyzed.
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