nltk-library
There are 5 repositories under nltk-library topic.
- hands-on-nltk-tutorial
hb20007 / hands-on-nltk-tutorial
The hands-on NLTK tutorial for NLP in Python
Language:Jupyter Notebook 539 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.
Language:Python 79TGDivy / MBTI-Personality-Classifier
A model which uses your social media posting predict your MBTI personality type.
Language:Jupyter Notebook 59luchux / ipython-notebook-nltk
An introduction to Natural Language processing using NLTK with python.
Language:Jupyter Notebook 19rohitthapliyal2000 / Amazon-Mobile-Sentiment-Analysis
Opinion mining of Mobile reviews on Amazon platform
Language:Python 19rsher60 / 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.
Language:Python 16caciitg / UTrack
UTrack analyses the user's tweets and finds the level of Loneliness, Stress and Anxiety, and their trends over time
Language:Python 14nilmolne / 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.
Language:Jupyter Notebook 11anubhavsaxena14 / Threat-Detection-using-Sentiment-Analysis
Threat Detection System using Hybrid (Machine Learning + Lexical Analysis) learning Approach.
Language:Python 9malares / 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.
Language:Jupyter Notebook 8- AIML-NLP-Text-Scoring
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.
Language:Python 8 Arsh2k01 / UTrack
UTrack analyses the user's tweets and finds the level of Loneliness, Stress, and Anxiety, and their trends over time
Language:Python 7- greek-dialect-classifier
Classifier that identifies Greek text as Cypriot Greek or Standard Modern Greek
Language:Jupyter Notebook 7 zuzannna / CoffeeFlavors
☕ Automatic tagging for coffee reviews using SCAA flavor wheel standardized tags 🤖☕
Language:Python 7anunay999 / image_captioning_vgg16
Imaging Captioning using VGG16
Language:Jupyter Notebook 6SOUMEE2000 / Applicant_Tracking_System
This streamlitapp is built for employers looking to match best candidate resumes against a particular job description.
Language:Python 6binnazcabuk / Sentiment-Analysis-in-Turkish-Film
Graduation Project/Sentiment Analysis in Turkish Film Reviews
Language:Python 5datalivre / web-scraping
Raspando o site www.letras.mus.br em busca de letras musicais (scraping e crawling).
Language:Python 5huseyincenik / nlp_natural_language_processing
NLP (Natural Language Processing)
Language:Jupyter Notebook 5MeqdadDev / articles-text-to-speech
Convert the text of articles to speech using nltk, newspaper and gTTS with Python.
Language:Python 5subhadeep-123 / Sentiment-Analysis
In this repository I have implemented Sentiment analysis in twitter dataset and dataset
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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.
Language:Python 4felipexw / guessb
Webapp para classificar comentários (positivos, negativos e neutros) advindos do Facebook usando Natural Language Toolkit (NLTK) + Django e Bootstrap na interface Web.
nltknltk-pythonnltk-librarynltk-dataknearest-neighbor-classifierknn-classificationknnrandom-forestdecision-tree-classifiergradient-boostinglogistic-regressionlogistic-regression-algorithmid3-algorithmgaussian-naive-bayes-implementationbernoulli-naive-bayesmultinomial-naive-bayesdjango-rest-frameworkbootstrapLanguage:Python 4jpsarkar / User_Location_Density_Categorization
Written in Python for SPARK for clustering customer based on geo-location to get various insight about behaviour
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shrutisaxena0617 / Yelp-Restaurant-Classification-and-Sentiment-Analysis
A deep dive into Yelp customers' reviews and ratings to perform sentiment analysis and classify restaurants
Language:Jupyter Notebook 4techcentaur / Chunking-NER
Chunking Data techniques in Named Entity Recognition(NER) using NLP libraries and algorithms
Language:Python 4Tomjg14 / Reddit-crawler
This repository is on the collection of comments from Reddit and how to work with those comments to perform sentiment analysis.
Language:Python 4Wiryco / Natural-Language-Analysis-And-Processing
Estudo sobre análise e processamento de linguagem natural usando a biblioteca NLTK
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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.
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tohid-yousefi / Sentiment_Analysis_on_Amazon_Product_Reviews
In this section, we will do a sentiment analysis on amazon product reviews.
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