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text_mining_resources

Resources for learning about Text Mining and Natural Language Processing
https://github.com/stepthom/text_mining_resources

Last synced: 3 days ago
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Blog Articles, Papers, Case Studies
- Sentiment Analysis
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Twitter Sentiment Analysis Overview - by-step walkthrough on how to perform sentiment analysis using TextBlob.
  - ELMO embeddings in Keras using Tensorflow Hub
  - ELMO embeddings in Keras using Tensorflow Hub
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - On the negativity of negation
  - Donald Trump vs Hillary Clinton: sentiment analysis on Twitter mentions
  - Does sentiment analysis work? A tidy analysis of Yelp reviews
  - From tweets to polls: Linking text sentiment to public opinion time series
  - Aspect Based Sentiment Analysis of Amazon Product Reviews
  - CACM: Techniques and Applications for Sentiment Analysis
  - Unsupervised Sentiment Analysis with Signed Social Networks
  - Lexicon-Based Methods for Sentiment Analysis - CAL (Semantic Orientation CALculator), a measure of subjectivity and opinion for sentimental analysis.
  - That Sentimental Feeling - syuzhet-validation/).
  - Unsupervised Sentiment Neuron
  - Sentiment Analysis Tools Overview, Part 1. Positive and Negative Words Databases
  - Sentiment analysis, Concept analysis and Applications
  - Twitter sentiment analysis using combined LSTM-CNN models
  - VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text - based model of sentiment analysis.
  - A comparison of Lexicon-based approaches for Sentiment Analysis of microblog posts - based approach for sentiment analysis of Twitter posts, based on lexical resources such as SentiWordNet.
  - Challenges in Sentiment Analysis
  - A survey on sentiment analysis challenges - seven papers.
  - Sentiment analysis on Trump's tweets using Python
  - Twitter mood predicts the stock market
  - Forbes: How Quant Traders Use Sentiment To Get An Edge On The Market
  - Sentdex: Quantifying the Qualitative
  - Lost at Sea: How Social Media is Helping Cruise Lines Attract Millennials
  - Harry Plotter: Celebrating the 20 year anniversary with tidytext and the tidyverse in R
  - Cannes Lions 2017: Hungerithm, Mars Chocolate Australia (Clemenger BBDO, Melbourne)
  - Sentiment analysis: 10 applications and 4 services
  - Sentiment Analysis of 2.2 million tweets from Super Bowl 51
  - Emotion and Sentiment Analysis: A Practitioner’s Guide to NLP
  - Streaming Analytics Tutorial on Azure
  - How to Analyze sentiment in Azure
  - how-to-perform-sentiment-analysis-using-python-tutorial/
  - Twitter Sentiment Analysis Overview - by-step walkthrough on how to perform sentiment analysis using TextBlob.
  - ELMO embeddings in Keras using Tensorflow Hub
  - Twitter Sentiment Analysis in Python using TextBlob
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Current State of Text Sentiment Analysis from Opinion to Emotion Mining
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis, Concept analysis and Applications
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis: 10 applications and 4 services
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Current State of Text Sentiment Analysis from Opinion to Emotion Mining
  - Sentiment analysis, Concept analysis and Applications
  - Breakthrough Research Papers and Models for Sentiment Analysis
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis: 10 applications and 4 services
  - Sentiment analysis, Concept analysis and Applications
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - What Your Boss Could Learn by Reading the Whole Company’s Emails - mails). Text analytics and NLP have become an increasingly popular approach to help search for clues that may indicate the level of employee engagement in the workplace, and any potential ‘red-flags’ that should receive particular attention by an organization and its ethical implications.
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - ELMO embeddings in Keras using Tensorflow Hub
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - Sentiment analysis, Concept analysis and Applications
  - Sentiment analysis: 10 applications and 4 services
  - ELMO embeddings in Keras using Tensorflow Hub
  - ELMO embeddings in Keras using Tensorflow Hub
  - ELMO embeddings in Keras using Tensorflow Hub
  - ELMO embeddings in Keras using Tensorflow Hub
  - ELMO embeddings in Keras using Tensorflow Hub
- Scraping
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Scraping HTML using Scrapy
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Extract text from any document; no muss, no fuss.
  - Using Scrapy to Build your Own Dataset
  - Using Scrapy to Build your Own Dataset
  - Extract text from any document; no muss, no fuss.
- Cleaning
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - Text Preprocessing in Python: Steps, Tools, and Examples
  - How to Clean Text for Machine Learning with Python - by-step guide of how to perform text data pre-processing.
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - Feature Extraction, Basic Pre-processing, and Advanced Processing
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - Text Preprocessing in Python: Steps, Tools, and Examples
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
  - How to solve 90% of NLP problems: a step-by-step guide
- Document Classification
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Machine Learning with Text in scikit-learn (PyCon 2016) - learn in the document classification process.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Naive Bayes and Text Classification - depth overview of both the Naive Bayes algorithm and how it can be used in the document classification process.
  - Bag of Tricks for Efficient Text Classification
  - Text Classifier Algorithms in Machine Learning
  - Classifying Documents in the Reuters-21578 R8 Dataset
  - Multi-Class Text Classification with Scikit-Learn - class problems, such as classifying consumer complaints into one of 12 categories.
  - Machine Learning with Text in scikit-learn (PyCon 2016) - learn in the document classification process.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Towards Explainable NLP: A Generative Explanation Framework for Text Classification
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
  - Text Classification in Python with scikit-learn and nltk - learn.
- Q&A Systems, Chatbots <a id="qa-systems"></a>
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - NLP — Building a Question Answering model
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Training Millions of Personalized Dialogue Agents
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - Task-oriented Dialogue System for Automatic Diagnosis
  - A Survey on Dialogue Systems: Recent Advances and New Frontiers
  - Examining the Impact of an Automated Translation Chatbot on Online Collaborative Dialog for Incidental L2 Learning
  - Generative Model Chatbots- May 2017
  - A Guide to Building a Multi-Featured Slackbot with Python- March 2017
  - The Road to a Conversational Banking Future-February 2019
  - Chatbots - Designing intents and entities for NLP Models
  - Li Deng at AI Frontiers: Three Generations of Spoken Dialogue Systems (Bots)
  - NLP — Building a Question Answering model
  - Meet Lucy: Creating a Chatbot Prototype
  - Microsoft Bot Framework
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - Building a Simple Chatbot from Scratch in Python (Using NLTK)- September 2018
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
  - NLP — Building a Question Answering model
  - NLP — Building a Question Answering model
  - Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
  - The Road to a Conversational Banking Future-February 2019
  - NLP — Building a Question Answering model
- Concept Analysis/Topic Modeling <a id="concept-analysis"></a>
  - Using LDA Topic Models as a Classification Model Input
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - Topic Modelling The Legal Subject Matter And Judicial Activity Of The High Court Of Australia, 1903–2015
  - Using LDA Topic Models as a Classification Model Input
  - Using LDA Topic Models as a Classification Model Input
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - How our startup switched from Unsupervised LDA to Semi-Supervised GuidedLDA
  - Topic models: Past, present, and future
  - Word vectors using LSA, Part - 2
  - Probabilistic Topic Models
  - LEGO color themes as topic models
  - Topic Modeling with LSA, PLSA, LDA & lda2Vec
  - text2vec's Description of Topic Models
  - Topic Modelling Portal
  - Applications of Topic Models
  - MACS 30500: Text analysis: topic modeling
  - COTA, Uber’s topic modelling approach to improving customer support
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Topic Modelling The Legal Subject Matter And Judicial Activity Of The High Court Of Australia, 1903–2015
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - NLP: Extracting the main topics from your dataset using LDA in minutes
  - Using LDA Topic Models as a Classification Model Input
  - Using LDA Topic Models as a Classification Model Input
  - Using LDA Topic Models as a Classification Model Input
  - Using LDA Topic Models as a Classification Model Input
  - Using LDA Topic Models as a Classification Model Input
- Text Summarization
  - Improving Abstraction in Text Summarization
  - Text Summarization and Categorization for Scientific and Health-Related Data
  - Text Summarization with Gensim
  - Unsupervised Text Summarization using Sentence Embeddings
- Transformers and Language Models
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - Understanding Large Language Models
  - A Primer in BERTology: What we know about how BERT works
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
  - Machines Beat Humans on a Reading Test. But Do They Understand?
  - The Illustrated Transformer
  - OpenAI: Better Language Models and Their Implications - trained Transformer-based unsupervised language model that achieves state-of-the-art on many language benchmarks with focus on text generation. Controversial limited release. February 14, 2019.
  - ChatGPT User Experience: Implications for Education
  - New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments
  - ChatGPT: Educational friend or foe? - Pasek and Blinkoff (Temple University). January 2023.
  - Don’t Ban ChatGPT in Schools. Teach With It.
  - ChatGPT and the Future of Business Education
  - Udemy course (January 2023). ChatGPT for Teachers in Education.
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - the transformer … “explained”?
  - Educators Battle Plagiarism As 89% Of Students Admit To Using OpenAI’s ChatGPT For Homework
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - A review of BERT based models
  - BERT Explained - State of the art language model for NLP
  - ChatGPT launch blog
- General <a id="general-articles"></a>
  - Crowdsourcing Ground Truth for Medical Relation Extraction
  - Why Text Mining May Be The Next Big Thing
  - SAS CEO offers analytics over BI, reveals use cases for text analytics
  - Value and benefits of text mining
  - Text Mining South Park - A Text Mining blog which covers on a variety of topics.
  - Natural Language Processing Tutorial
  - An Introduction to Text Mining using Twitter Streaming API and Python
  - How To Get Into Natural Language Processing
  - Creating machine learning models to analyze startup news - Part1 - machine-learning-models-analyze-news/). [Part 3](https://monkeylearn.com/blog/analyzing-startup-news-with-machine-learning/).
  - Comparison of the Most Useful Text Processing APIs
  - Natural language based financial forecasting: a survey
  - 5 Heroic Tools for Natural Language Processing
  - Natural Language Processing unlocks hidden data to transform healthcare efficiency, quality and cost
  - Extracting medical problems from electronic clinical documents
  - Natural Language Processing (NLP) for Machine Learning
  - How to Write a Spelling Corrector - by Peter Norvig
  - Using AI to unleash the power of unstructured government data - to-comprehend primer and background on NLP, and the various applications NLP could be used on unstructured Government text data. The article includes many US Government examples on how NLP is currently deployed across different domains (e.g. to help analyze public feedback/sentiment analysis/topic modelling, to improve forensic investigations, to aid in Government policy-making and regulatory compliance). The key point is to apply different NLP techniques to explore and uncover key Government intelligence insights.
  - Lessons learned building natural language processing systems in health care
  - How Algorithms Know What You’ll Type Next
  - NLP in healthcare
  - AI Harvard Business Review
  - Why Accuracy in Natural Language Processing is Crucial to the Future of AI in Retail
  - Natural Language Processing is Fun! How computers understand Human Language
  - WEF Live Campaign - Twitter fed Global News Topics & Sentiment Tracker - Live Jan 2019
  - The Definitive Guide to Natural Language Processing
  - From Natural Language to Calendar Entries, with Clojure
  - Ask HN: How Can I Get into NLP (Natural Language Processing)?
  - Ask HN: What are the best tools for analyzing large bodies of text?
  - Quora: How do I learn Natural Language Processing?
  - Quora Topic: Natural Language Processing
  - The Definitive Guide to Natural Language Processing
  - Futures of text
  - Where to start in Text Mining
  - Text Mining in R and Python: 8 Tips To Get Started
  - An introduction to text analysis with Python, Part 1
  - Mining Twitter Data with Python (Part 1: Collecting Data)
  - Ask HN: How Can I Get into NLP (Natural Language Processing)?
  - R or Python on Text Mining
  - Natural Language Processing: An Introduction
  - Extracting Features of Entertainment Products: A Guided Latent Dirichlet Allocation Approach Informed by the Psychology of Media Consumption - level consumption.” This academic article provides both a framework and managerial implications that suggest the application of LDA and NLP for feature extraction in entertainment products that can aid in traditional content-based consumer behavior models, and relevant marketing models applied to the media and entertainment industry.
  - Crowdsourcing Ground Truth for Medical Relation Extraction
- Word and Document Embeddings
  - Word2vec: fish + music = bass
  - The Current Best of Universal Word Embeddings and Sentence Embeddings
  - An Intuitive Understanding of Word Embeddings: From Count Vectors to Word2Vec
  - An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation
  - Document Embedding with Paragraph Vectors
  - GloVe Word Embeddings Demo
  - Text Classification With Word2Vec
  - Document Embedding
  - From Word Embeddings To Document Distances
  - Word Embeddings, Bias in ML, Why You Don't Like Math, & Why AI Needs You
  - Word Vectors in Natural Language Processing: Global Vectors (GloVe)
  - Doc2Vec Tutorial on the Lee Dataset
  - Word Embeddings in Python with SpaCy and Gensim
  - Deep Contextualized Word Represenations - tf)
  - Universal Language Model Fine-tuning for Text Classification
  - Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
  - Learned in Translation: Contextualized Word Vectors
  - Distributed Representations of Sentences and Documents - technologies.com/doc2vec-tutorial/)
  - sense2vec
  - Skip Thought Vectors
  - The Amazing Power of Word Vectors
  - Contextual String Embeddings for Sequence Labeling
  - A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks - task learning approach for a set of interrelated NLP tasks. Presented at AAAI conference in January 2019.[Implementation code](https://github.com/huggingface/hmtl).
  - An Idiot’s Guide to Word2vec Natural Language Processing
  - Word2vec: fish + music = bass
  - Universal Sentence Encoder Visually Explained
  - NLP's ImageNet moment has arrived - trained NLP language models, drawing parallels to ImageNet's contributions to computer vision.
  - Get Busy with Word Embeddings- An Introduction (February 2018)
- Biases in NLP
  - AI bias: It is the responsibility of humans to ensure fairness
  - Venturebeat Blogpost - Gender biases in datasets - Based on UCLA research paper "Learning Gender Neutral Word Embeddings" Aug 2018.
  - Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
  - Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
  - Venturebeat Blogpost - Gender biases in datasets - Based on UCLA research paper "Learning Gender Neutral Word Embeddings" Aug 2018.
- Stop Words
  - Removing stop words with NLTK in Python
  - TEXT CLASSIFICATION FOR SENTIMENT ANALYSIS – STOPWORDS AND COLLOCATIONS
- Dimensionality Reduction
  - Taming Text with the SVD
  - Dimensionality Reduction for Bag-of-Words Models: PCA vs LSA
  - An introduction to Bag of Words and how to code it in Python for NLP
  - Bag of Words and Tf-idf Explained
- Machine Translation
  - The Annotated Transformer - by-line implementation of "Attention Is All You Need".
  - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - research/bert). [Pytorch port.]( https://github.com/codertimo/BERT-pytorch)
  - Phrase-Based & Neural Unsupervised Machine Translation - based model. Awarded as the Best Paper Award at EMNLP 2018. [Implementation code](https://github.com/facebookresearch/UnsupervisedMT).
  - Blog Post: Found in translation: More accurate, fluent sentences in Google Translate
  - NYTimes: The Great A.I. Awakening
  - Machine Learning Translation and the Google Translate Algorithm
  - Paper Dissected: “Attention is All You Need” Explained
- Fuzzy Matching, Probabilistic Matching, Record Linkage, Etc. <a id="fuzzy-matching"></a>
  - agrep method in R
  - fuzzywuzzy package in R
  - Fuzzy merge in R
  - Learning Text Similarity with Siamese Recurrent Networks
  - Fuzzy String Matching – a survival skill to tackle unstructured information
- Stemming
  - What is the Difference Between Stemming and Lemmatization?
  - Stemming and Lemmatization in Python
  - Sentiment Symposium Tutorial: Stemming
  - Article: Text Stemming: Approaches, Applications, and Challenges
  - Stemming and Lemmatization in Python
- Sarcasm Detection
  - CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
  - A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
  - Detecting Sarcasm with Deep Convolutional Neural Networks
- Entity and Information Extraction
  - Entity Extraction and Network Analysis
  - Natural Language Processing for Information Extraction
  - NLP Techniques for Extracting Information - depth exploration of the seven steps framework of NLP data mining tools and techniques.
  - NLP Techniques for Extracting Information - depth exploration of the seven steps framework of NLP data mining tools and techniques.
- Document Clustering and Document Similarity
  - Text Clustering: Get quick insights from Unstructured Data
  - Document Clustering
  - Document Clustering: A Detailed Review
  - Text mining and sentiment analysis on video game user reviews using SAS® Enterprise Miner
  - Who wrote the anti-Trump New York Times op-ed? Using tidytext to find document similarity
  - Text Clustering: Get quick insights from Unstructured Data
  - Text mining and sentiment analysis on video game user reviews using SAS® Enterprise Miner
- Knowledge Graphs
  - WTF is a knowledge graph?
  - A survey of graphs in natural language processing
  - Using fastText and Comet.ml to classify relationships in Knowledge Graphs
- Deep Learning
  - Keras LSTM tutorial – How to easily build a powerful deep learning language model
  - Deep Learning for Natural Language Processing: Tutorials with Jupyter Notebooks
  - A Survey of the Usages of Deep Learning in Natural Language Processing
  - Sequence Classification with Human Attention - tracking corpora to regularize attention in recurrent neural networks (RNN). [Implementation code](https://github.com/coastalcph/Sequence_classification_with_human_attention).
  - Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python
  - Multi-Task Deep Neural Networks for Natural Language Understanding
  - Deep Learning for Sentiment Analysis : A Survey
  - NEURAL READING COMPREHENSION AND BEYOND - Reading comprehension models built on top of deep neural networks.
  - A STRUCTURED SELF-ATTENTIVE SENTENCE EMBEDDING
  - Investigating Capsule Networks with Dynamic Routing for Text Classification
  - Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
  - TWITTER SENTIMENT ANALYSIS USING CAPSULE NETS AND GRU
  - Identifying Aggression and Toxicity in Comments using Capsule Network
  - Dynamic Routing Between Capsules
  - MATRIX CAPSULES WITH EM ROUTING
  - Microsoft: Multi-Task Deep Neural Network (MT-DNN)
Books
Products
- Knowledge Graphs
  - Dialogflow
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - Systran - Enterprise Translation Products
  - Alchemy API
  - brat
  - Lyrebird.ai - Realistic Voice Cloning and Text-to-Speech” recognition platform. This Canadian start-up has created a product/platform that syncs both voice cloning with text-to-speech. Lyrebird recognizes the intonations and voice patterns from audio recordings, and overlays text data input to recreate a text-to-speech audio file output from the selected voice pattern audio recording.
  - Ask Data by Tableau Software Inc. - on to help assist existing Tableau platform users with retrieving quick and easy data visualizations to drive business intelligence insights. Similar to a search engine user interface, Tableau’s Ask Data feature interface applies NLP from user text input to extract key words to find data analytics and business insights quickly on the Tableau Platform.
  - Weka - to-use, graphical Machine Learning Workbench including NLP capabilities.
  - Microsoft Azure Text Analytics
  - Amazon Lex
  - Amazon Comprehend
  - Apache PDFBox
  - SO: How to extract text from a PDF?
  - Tools for Extracting Data and Text from PDFs - A Review
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - SAS Sentiment Analysis
  - STATISTICA
  - Text Mining (Big Data, Unstructured Data)
  - Gate
  - Video: How IBM Watson learns (3 minutes)
  - Video: IBM Watson on Jeapardy! (10 minutes)
  - Video: IBM Watson: The Science Behind an Answer (7 minutes)
  - Stocktwits
  - Meltwater
  - Lexalytics Sematria
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - Lyrebird.ai - Realistic Voice Cloning and Text-to-Speech” recognition platform. This Canadian start-up has created a product/platform that syncs both voice cloning with text-to-speech. Lyrebird recognizes the intonations and voice patterns from audio recordings, and overlays text data input to recreate a text-to-speech audio file output from the selected voice pattern audio recording.
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
  - How I used NLP (SpaCy) to screen Data Science Resumes
Misc
- Lexicons for Sentiment Analysis
Online courses
- Knowledge Graphs
Datasets
- Knowledge Graphs
  - The Multi-Genre NLI Corpus
  - MIMIC
  - Clinical NLP Dataset Repository - available clinical datasets for use in NLP research.
  - Twitter US Airline Sentiment
  - DuoRC - answer pairs with evaluation script for Paraphrased Reading Comprehension
  - EDGAR Financial Statements
  - UCI's Text Datasets
  - data.world's Text Datasets
  - Insight Resources Datasets
  - Consumer Complaint Database
  - Sentiment Labelled Sentences Data Set
  - Amazon product data
  - Data is Plural
  - FiveThirtyEight's datasets
  - r/datasets
  - R's `datasets` package
  - 200,000 Russian Troll Tweets - Released by Congress from Twitter suspended accounts and removed from public view.
  - Wikipedia: List of datasets for ML research
  - Kaggle: UMICH SI650 - Sentiment Classification
  - Lee's Similarity Data Sets
  - 15 Best Chatbot Datasets for Machine Learning
  - A Survey of Available Corpora for Building Data-Driven Dialogue Systems
  - First Quora Dataset Release: Question Pairs
  - The Best 25 Datasets for Natural Language Processing
  - American National Corpus Download
  - Awesome Twitter
  - The Big Bad NLP Database
  - CBC News Coronavirus articles
  - Huggingface
  - Corpus of Presidential Speeches (CoPS) and a Clinton/Trump Corpus
  - 15 Best Chatbot Datasets for Machine Learning
  - 15 Best Chatbot Datasets for Machine Learning
  - Million Song Lyrics - Of-Words (BOW) format.
- Lexicons for Sentiment Analysis
  - MPQA Lexicon
  - SentiWordNet
  - nrc
  - Bing
Blogs
Online Demos and Tools
- Knowledge Graphs
  - Stanford Parser
  - Stanford CoreNLP
  - word2vec demo
  - sense2vec: Semantic Analysis of the Reddit Hivemind
  - RegexPal
  - Cognitive Computation Group - Part of Speech Tagging Demo - of-speech tagging, information extraction tasks etc.
  - Cognitive Computation Group - Part of Speech Tagging Demo - of-speech tagging, information extraction tasks etc.
APIs and Libraries
- Knowledge Graphs
  - LDAvis
  - keras - level neural networks 'API'. ([RStudio Blog: TensorFlow for R](https://blog.rstudio.com/2018/02/06/tensorflow-for-r))
  - Spark NLP - grade, scalable, and trainable versions of the latest research in natural language processing.
  - Apache Spark - purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs.
  - MLlib
  - LDA
  - h5py
  - HDF5
  - Introduction to StanfordNLP: An Incredible State-of-the-Art NLP Library for 53 Languages (with Python code)
  - Stanford Parser
  - Stanford POS Tagger - of-Speech tagger.
  - Stanford Named Entity Recognizer
  - Comparison of Top 6 Python NLP Libraries
  - R packages
  - tm
  - lsa
  - lda
  - textir
  - corpora
  - tau
  - sentimentr - based sentiment analysis.
  - cleanNLP - based sentiment analysis.
  - RSentiment - based sentiment analysis. Contains support for negation detection and sarcasm.
  - text2vec - friendly tools for text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), similarities.
  - fastTextR
  - retweet - recipes/))
  - topicmodels
  - textmineR
  - gtrendsR
  - Analyzing Google Trends Data in R
  - textstem
  - NLPutils
  - Udpipe
  - Python modules
  - Tutorial
  - Spark NLP - grade, scalable, and trainable versions of the latest research in natural language processing.
  - spaCy - Strength Natural Language Processing in Python.
  - textblob
  - Natural Language Basics with TextBlob
  - Gensim
  - Pattern.en - of-speech tagger for English, sentiment analysis, tools for English verb conjugation and noun singularization & pluralization, and a WordNet interface.
  - textmining
  - Scrapy
  - PyText - learning based NLP modeling framework built on PyTorch.
  - Beautiful Soup
  - embeddings
  - fastText
  - polyglot
  - textacy
  - Stanford Classifier
  - Stanford Topic Modeling Toolbox
  - Apache OpenNLP
  - TextRazor API
  - pyCaret's NLP Module - code machine learning library in Python that aims to reduce the cycle time from hypothesis to insights; also, PyCaret's Founder Moez Ali is a Smith Alumni - MMA 2020.
  - TFIDF - inverse document frequency
  - retweet - recipes/))
Other Curated Lists
- Lexicons for Sentiment Analysis
Major NLP Conferences
- Knowledge Graphs
Benchmarks
- Knowledge Graphs
  - SQuAD 1.0 paper
  - SQuAD 2.0 paper
  - GLUE leaderboard
  - GLUE paper - sentence tasks (e.g. check if grammar is correct, sentiment analysis), similarity and paraphrase tasks (e.g. determine if two questions are equivalent), and inference tasks (e.g. determine whether a premise contradicts a hypothesis).

Programming Languages

Categories

Blog Articles, Papers, Case Studies 603 Products 59 APIs and Libraries 56 Misc 45 Datasets 37 Online courses 32 Books 31 Online Demos and Tools 7 Major NLP Conferences 6 Blogs 4 Benchmarks 4 Other Curated Lists 4 License 1

Sub Categories

Knowledge Graphs 200 Sentiment Analysis 121 Q&A Systems, Chatbots <a id="qa-systems"></a> 111 Concept Analysis/Topic Modeling <a id="concept-analysis"></a> 74 Transformers and Language Models 65 Lexicons for Sentiment Analysis 54 General <a id="general-articles"></a> 41 Cleaning 37 Document Classification 33 Scraping 28 Word and Document Embeddings 28 Deep Learning 16 Document Clustering and Document Similarity 7 Machine Translation 7 Biases in NLP 5 Stemming 5 Fuzzy Matching, Probabilistic Matching, Record Linkage, Etc. <a id="fuzzy-matching"></a> 5 Dimensionality Reduction 4 Text Summarization 4 Entity and Information Extraction 4 Sarcasm Detection 3 Stop Words 2