nltk-library
There are 316 repositories under nltk-library topic.
hb20007/hands-on-nltk-tutorial
The hands-on NLTK tutorial for NLP in Python
abhishek305/PyBot-A-ChatBot-For-Answering-Python-Queries-Using-NLP
Pybot can change the way learners try to learn python programming language in a more interactive way. This chatbot will try to solve or provide answer to almost every python related issues or queries that the user is asking for. We are implementing NLP for improving the efficiency of the chatbot. We will include voice feature for more interactivity to the user. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.The main issue with text data is that it is all in text format (strings). However, the Machine learning algorithms need some sort of numerical feature vector in order to perform the task. So before we start with any NLP project we need to pre-process it to make it ideal for working. Converting the entire text into uppercase or lowercase, so that the algorithm does not treat the same words in different cases as different Tokenization is just the term used to describe the process of converting the normal text strings into a list of tokens i.e words that we actually want. Sentence tokenizer can be used to find the list of sentences and Word tokenizer can be used to find the list of words in strings.Removing Noise i.e everything that isn’t in a standard number or letter.Removing Stop words. Sometimes, some extremely common words which would appear to be of little value in helping select documents matching a user need are excluded from the vocabulary entirely. These words are called stop words.Stemming is the process of reducing inflected (or sometimes derived) words to their stem, base or root form — generally a written word form. Example if we were to stem the following words: “Stems”, “Stemming”, “Stemmed”, “and Stemtization”, the result would be a single word “stem”. A slight variant of stemming is lemmatization. The major difference between these is, that, stemming can often create non-existent words, whereas lemmas are actual words. So, your root stem, meaning the word you end up with, is not something you can just look up in a dictionary, but you can look up a lemma. Examples of Lemmatization are that “run” is a base form for words like “running” or “ran” or that the word “better” and “good” are in the same lemma so they are considered the same.
TGDivy/MBTI-Personality-Classifier
A model which uses your social media posting predict your MBTI personality type.
luchux/ipython-notebook-nltk
An introduction to Natural Language processing using NLTK with python.
rohitthapliyal2000/Amazon-Mobile-Sentiment-Analysis
Opinion mining of Mobile reviews on Amazon platform
rsher60/Sentiment-Analysis-by-combining-Machine-Learning-and-Lexicon-Based-methods
This project is on twitter sentimental analysis by combining lexicon based and machine learning approaches. A supervised lexicon-based approach for extracting sentiments from tweets was implemented. Various supervised machine learning approaches were tested using scikit-learn libraries in python and implemented Decision Trees and Naive Bayes techniques.
caciitg/UTrack
UTrack analyses the user's tweets and finds the level of Loneliness, Stress and Anxiety, and their trends over time
nilmolne/Text-Mining-The-New-York-Times-Articles
Provides the tools needed to mine text from The New York Times online articles by using Python and a dictionary text mining technique, given a time range and country of interest.
anubhavsaxena14/Threat-Detection-using-Sentiment-Analysis
Threat Detection System using Hybrid (Machine Learning + Lexical Analysis) learning Approach.
malares/STeM-Scientifc-Paper-Mining-Tool
STeM is a text mining tool to help scientists and researchers evaluate new papers in their area of interest. The program was born out of a desire to easily analyze scientific papers and to help scientists or researchers to decide whether the paper is interesting or not.
ali-mohammed-khair-nasser/Text-Summarizer-and-Categorical
Text processing and summarize with the category web application for Arabic and English texts using NLTK, Python, Flask, and other web languages.
weblineindia/AIML-NLP-Text-Scoring
A Python based AI ML package for generating the best matching text from a paragraph for a given keyword/sentence.
Arsh2k01/UTrack
UTrack analyses the user's tweets and finds the level of Loneliness, Stress, and Anxiety, and their trends over time
hb20007/greek-dialect-classifier
Classifier that identifies Greek text as Cypriot Greek or Standard Modern Greek
zuzannna/CoffeeFlavors
☕ Automatic tagging for coffee reviews using SCAA flavor wheel standardized tags 🤖☕
anunay999/image_captioning_vgg16
Imaging Captioning using VGG16
SOUMEE2000/Applicant_Tracking_System
This streamlitapp is built for employers looking to match best candidate resumes against a particular job description.
binnazcabuk/Sentiment-Analysis-in-Turkish-Film
Graduation Project/Sentiment Analysis in Turkish Film Reviews
datalivre/web-scraping
Raspando o site www.letras.mus.br em busca de letras musicais (scraping e crawling).
huseyincenik/nlp_natural_language_processing
NLP (Natural Language Processing)
MeqdadDev/articles-text-to-speech
Convert the text of articles to speech using nltk, newspaper and gTTS with Python.
subhadeep-123/Sentiment-Analysis
In this repository I have implemented Sentiment analysis in twitter dataset and dataset
Aman83770/Briefly
A Text Summerization web app made on Flask framework
buseyaren/Logistic-Regression-with-NLTK
This Github repository states that sentiment analysis was performed on the "twitter140" dataset with Logistic Regression method using NLTK for English language.
felipexw/guessb
Webapp para classificar comentários (positivos, negativos e neutros) advindos do Facebook usando Natural Language Toolkit (NLTK) + Django e Bootstrap na interface Web.
jpsarkar/User_Location_Density_Categorization
Written in Python for SPARK for clustering customer based on geo-location to get various insight about behaviour
nirdesh17/movie-recommender-system
A movie recommendation system, is an AI/ML-based approach to filtering or predicting the users’ film preferences based on their past choices and behavior. It’s an advanced filtration mechanism that predicts the possible movie choices of the concerned user and their preferences towards a domain-specific item, aka movie.
noufpy/ok-nouf
My simulated 3D scanned digital avatar
shrutisaxena0617/Yelp-Restaurant-Classification-and-Sentiment-Analysis
A deep dive into Yelp customers' reviews and ratings to perform sentiment analysis and classify restaurants
techcentaur/Chunking-NER
Chunking Data techniques in Named Entity Recognition(NER) using NLP libraries and algorithms
Tomjg14/Reddit-crawler
This repository is on the collection of comments from Reddit and how to work with those comments to perform sentiment analysis.
Wiryco/Natural-Language-Analysis-And-Processing
Estudo sobre análise e processamento de linguagem natural usando a biblioteca NLTK
aifenaike/Customer-Review-Sentiment-Analysis
A sentiment analysis model for food product reviews by customers.
mansourshebli/nltkPython
In this repo I provided simple examples to demonstrate how the the fundamentals of NLP on the NLTK library in Python works; Tokenization, Stopword Removal, Parts of Speech Tagging, Named Entity Recognition, Sentiment Analysis using VADER. For better understanding check this NLTK documentation:
Rajspeaks/Machine-Learning-Approach-to-English-POS-Tagging-using-NLTK
Machine Learning approach to English Corpus POS Tagging using NLTK. A mini project under the mentorship of Prof. Sandipan Ganguly, HIT-K.
tohid-yousefi/Sentiment_Analysis_on_Amazon_Product_Reviews
In this section, we will do a sentiment analysis on amazon product reviews.