Collection and resources for Bulgarian Corpus, Datasets and Models used in ASR, TTS or NLP tasks together with the links of corresponding tools/apps.

Contains some basic primitive implementations of NLP concepts.

Text Tonsorium - a toolbox that automatically arranges NLP tools in workflows and enacts them with user's inputs

Natural language processing is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human languages, in particular how to program computers to process and analyze large amounts of natural language data

A sentiment analysis based on the Amazon food reviews dataset.

A NLP app built with streamlit framework using SpaCy for Tokenization,Lemmatization,Parts of Speech (POS) Tagging and Named Entity Extraction(NER),TextBlob for Sentiment Analysis.

This project applies Text Mining techniques using Python (NLTK, spaCy, TextBlob) to analyze a book. It includes text cleaning, tokenization, sentiment analysis, and keyword extraction to uncover insights.

Collection of basic Natural Language Processing examples that cover essential techniques like tokenization, text representation, and text classification.

Classifying a tweet as positive, neutral, or negative sentiment using Natural Language Processing (CBOW approaches) and Traditional Machine Learning Algorithms.

Fake News Detection** is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake.

Sentiment mining

NLP Explorer is a repository dedicated to exploring and applying various Natural Language Processing (NLP) techniques using powerful tools like spaCy, BERT, and Python libraries. This project is a part of my learning journey as an engineering student focused on AI and language understanding.

This repo is about Natural Language Processing (NLP).

Tokenizador y Lematizador de palabras

Text Processing performed on the Apple Macbook for feature extraction

Information retrieval system based on the word embedding technique (word2vec)

NLP Spam Classifier Model to separate out Spam messages from legitimate messages.

Analyzing Of Tweet Sentiments of Covid-19 Dataset Using Supervised Learning Classification Algorithms

Reduce manual effort in HRM Department using NLP

This project performs sentiment analysis on a Twitter dataset, aiming to classify tweets into positive, negative, or neutral sentiments. Sentiment analysis is crucial for understanding public opinion on various topics, brands, or events based on social media data.