cntext 是一个专为社会科学实证研究设计的中文文本分析 Python 库。它不仅提供传统的词频统计和情感分析，还支持词嵌入训练、语义投影计算等高级功能，帮助研究者从大规模非结构化文本中测量抽象构念——如态度、认知、文化观念与心理状态。

Spanish word embeddings computed with different methods and from different corpora

Extracting Skills from resume using Machine Learning

A multi-source supplier risk dataset combining KG scores, semantic similarity, and external disruption signals ideal for supply chain resilience research. Useful for researchers working on supplier risk assessment, multi-objective optimization, or reinforcement learning in dynamic sourcing environments.

The backed for an anime recommender system that combines multiple methods to provide a variety of recommendations to users based on different similarity metrics

Automatic library of congress classification, using word embeddings from book titles and synopses.

Information retrieval course project

Sentiment Analysis on Loksabha Elections 2019

create the search engine to retrieve the text documents. (the information retrieval course project)

Text classification is the task of assigning a set of predefined categories to free text. Text classifiers can be used to organize, structure, and categorize pretty much anything. For example, new articles can be organized by topics, support tickets can be organized by urgency, chat conversations can be organized by language, brand mentions can be organized by sentiment, and so on.

M.Sc. mini project for NLP class (M908)

Bengali word embedding using BengaliWord2Vec from BNLP. A mini project under the mentorship of Prof. Sandipan Ganguly, HIT-K.

An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks

I created a new technique to do sentiment analysis with 98% probability using multiple techniques combined to from a new method. I made a video on this whole project and show you, how “Next Gen Sentiment” is much better then NLTK, TEXTBLOB or LLMs.

Checkout my adventures into NLP here.

This is final project of Information Retrieval course which is implementation of a search engine

Arabic Word Embedding models SkipGram, and GLoVE are trained over Arabic Wiki data Dump 2018 dataset from scratch using Gensim and GLoVE python libraries. Then the models are evaluated on three NLP tasks and its results are visualized in T-SNE

Automatic library of congress classification, using word embeddings from book titles and synopses.

My work as machine learning intern. Unsupervised Text Clustering and Topic Modeling

Using LSTM model to classify text into fake or real

AI, Innovation, and Growth Final Project

NLP demos and talks made with Jupyter Notebook and reveal.js

Patent Big Data Analysis Platform for Individual Patent Applicants

In this project we will be building a text classifier using LSTM and Wor2vec

Sentiment analysis is the process of detecting positive or negative sentiment in text. It’s often used by businesses to detect sentiment in social data, gauge brand reputation, and understand customers.

Comparison of contextual (BERT) and uncontextual (GloVe and Word2Vec) word embeddings in the task of music genre classification from lyrics.

Projects of Machine learning and Deep learning

Arabic part of speech tagging using arabic PUD dataset using bidirectioanl LSTM for sequential labeling classification

[FR - Duo] 2023 - 2024 Centrale Méditerranée AI Master | NLP project about embeddings and word2vec algorithm

🎬 Analyze movie reviews sentiment in real-time with "Sentiment Analysis on Movie Reviews using Word2Vec"! Powered by advanced NLP and deployed using Streamlit, this app categorizes reviews as positive or negative. Perfect for film enthusiasts and industry professionals! 🍿📊