text-preprocessing

🧹 Python package for text cleaning

Preprocessing Library for Natural Language Processing

Text preprocessing, representation, similarity calculation, text search and classification. Let's go and play with text!

This sentiment analysis project determines whether the tweets posted in the Turkish language on Twitter are positive or negative.

A repository to bind mecab for Python 3.5+. Not using swig nor pybind. (Not Maintained Now)

Basic text preprocessing for Bahasa with Python.

Text Preprocessing in Python

A python package for text preprocessing task in natural language processing.

This python module is an easy-to-use port of the text normalization used in the paper "Not low-resource anymore: Aligner ensembling, batch filtering, and new datasets for Bengali-English machine translation". It is intended to be used for normalizing / cleaning Bengali and English text.

Learning Machine Learning and showcasing my work for 100 Days.

Easy NLP in Python

2020 Açık Seminer - Turkish NLP workshop

Text Preprocessing Package includes cleaning, tokenization, dataset preparation ...etc

VIP Machine Learning Exercises and Practices

Vector Space based Search Engine for Arxiv Research Publications

MNLP: Mongolian Natural Language Processing.

My version of topic modelling using Latent Dirichlet Allocation (LDA) which finds the best number of topics for a set of documents using ldatuning package which comes with different metrics

My 2020 project focusing on NLP - Information Extraction

Quick and Simple Approach for Detecting Hate Speech in Arabic Tweets.

Text preprocessing in Python. Libs include string, re, nltk, spacy, gensim, textblob, unidecode, autocorrect, pyspellchecker

Performs tokenization, stemming, lemmatization, index creation, index compression and ranked retrieval of Cranfield documents

my exercises of course natural language processing datacamp

Hotels play a crucial role in travelling and with the increased access to information new pathways of selecting the best ones emerged. With this model, you can explore what makes a great hotel and maybe even use this model in your trip planning.

A tool for extracting chapters from Gutenberg Project Italian raw text e-books. RegEx are used to match chapter headings and extract the text between them.

We have to build a machine learning model to predict whether two questions asked on quora are similar or not . So that the similar questions asked may have the same answers which have been given earlier for the previously asked similar question.

Assignment-11-Text-Mining-01-Elon-Musk, Perform sentimental analysis on the Elon-musk tweets (Exlon-musk.csv), Text Preprocessing: remove both the leading and the trailing characters, removes empty strings, because they are considered in Python as False, Joining the list into one string/text, Remove Twitter username handles from a given twitter text. (Removes @usernames), Again Joining the list into one string/text, Remove Punctuation, Remove https or url within text, Converting into Text Tokens, Tokenization, Remove Stopwords, Normalize the data, Stemming (Optional), Lemmatization, Feature Extraction, Using BoW CountVectorizer, CountVectorizer with N-grams (Bigrams & Trigrams), TF-IDF Vectorizer, Generate Word Cloud, Named Entity Recognition (NER), Emotion Mining - Sentiment Analysis.