____ ____ ____ ____ _________ ____ ____ ____ ____ ____ ____ 
||t |||e |||x |||t |||       |||m |||i |||n |||i |||n |||g ||
||__|||__|||__|||__|||_______|||__|||__|||__|||__|||__|||__||
|/__\|/__\|/__\|/__\|/_______\|/__\|/__\|/__\|/__\|/__\|/__\|

A curated list of resources for learning about natural language processing, text mining, text analytics, and unstructured data.

• Books
  • R
  • Python
  • General
• Blogs
• Blog articles, Papers, Case Studies
  • General
  • Biases in NLP
  • Scraping
  • Cleaning
  • Stemming
  • Dimensionality Reduction
  • Sarcasm Detection
  • Document Classification
  • Entity and Information Extraction
  • Document Clustering and Document Similarity
  • Concept Analysis/Topic Modeling
  • Sentiment Analysis
  • Text Summarization
  • Machine Translation
  • Q&A Systems, Chatbots
  • Fuzzy Matching, Probabilistic Matching, Record Linkage, Etc.
  • Word and Document Embeddings
  • Deep Learning
  • Knowledge Graphs
• Benchmarks
• Online Courses
• APIs and Libraries
• Products
• Online Demos and Tools
• Datasets
• Misc
• Meta
• Other Curated Lists

• Text Mining with R
• Mastering Text Mining with R
• Text Mining in Practice with R

• Natural Language Processing with Python
• Natural Language Processing with PyTorch
• Python Natural Language Processing
• Mastering Natural Language Processing with Python
• Natural Language Processing: Python and NLTK
• Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning
• Deep Learning with Text
• Applied Natural Language Processing With Python (2018)

• Taming Text
• Speech and Language Processing
• Foundations of Statistical Natural Language Processing
• Language Processing with Perl and Prolog: Theories, Implementation, and Application (Cognitive Technologies)
• An introduction for information retrieval
• Handbook of Natural Language Processing
• Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
• Fundamentals of Predictive Text Mining
• Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More
• Neural Network Methods for Natural Language Processing
• Text Mining: A Guidebook for the Social Sciences
• Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence
• Neural Network Methods in Natural Language Processing
• Machine Learning for Text (2018)
• Towards Explainable NLP: A Generative Explanation Framework for Text Classification
• Natural Language Processing in Spanish

• Probably Approximately a Scientific Blog
• Sebastian Ruder
• NLP-progress
• natural language processing blog
• Text Mining, Analytics & More
• FriendlyData blog
• Salmon Run
• Lekta Blog
• NLP News

• Natural Language Processing is Fun! How computers understand Human Language. 2018.
• WEF Live Campaign - Twitter fed Global News Topics & Sentiment Tracker - Live Jan 2019
• Modern Deep Learning Techniques Applied to Natural Language Processing
• The Definitive Guide to Natural Language Processing
• From Natural Language to Calendar Entries, with Clojure. March 2015. NLP, 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 October 2015.
• Futures of text Feb 2015.
• R or Python on Text Mining Aug 2015.
• Where to start in Text Mining Aug 2012.
• Text Mining in R and Python: 8 Tips To Get Started. Oct 2016
• An introduction to text analysis with Python, Part 1 April 2012.
• Mining Twitter Data with Python (Part 1: Collecting Data)
• Why Text Mining May Be The Next Big Thing. March 2012.
• SAS CEO offers analytics over BI, reveals use cases for text analytics June 2011.
• Value and benefits of text mining. Sep 2015.
• Text Mining South Park Feb 2016
• Natural Language Processing: An Introduction
• Natural Language Processing Tutorial. June 2013.
• Natural Language Processing blog.
• An Introduction to Text Mining using Twitter Streaming API and Python
  • GitHub repo with code: https://github.com/adilmoujahid/Twitter_Analytics
• How To Get Into Natural Language Processing'
• Betty: a friendly English-like interface for your command line.
• Creating machine learning models to analyze startup news - Part1. Part 2. Part 3.
• Comparison of the Most Useful Text Processing APIs
• 100 Must-Read NLP Papers
• Crowdsourcing Ground Truth for Medical Relation Extraction
• Natural language based financial forecasting: a survey

• Venturebeat Blogpost - Gender biases in datasets - Based on UCLA research paper "Learning Gender Neutral Word Embeddings" Aug 2018 .

• Scraping HTML using Scrapy Tutorial on using the Python module Scrapy for easy data extraction from messy HTML websites.
• Extract text from any document; no muss, no fuss. July 2014.
• Using Scrapy to Build your Own Dataset Sep 2017.

• How to solve 90% of NLP problems: a step-by-step guide Jan 2018. A step by step guide on data cleaning and exploration for successful NLP model building.

• Removing stop words with NLTK in Python
• TEXT CLASSIFICATION FOR SENTIMENT ANALYSIS – STOPWORDS AND COLLOCATIONS

• Article: Text Stemming: Approaches, Applications, and Challenges
• What is the Difference Between Stemming and Lemmatization?
• Stemming and Lemmatization in Python
• Sentiment Symposium Tutorial: Stemming

• Taming Text with the SVD. SAS. Jan 2004.
• 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

• Automatic Sarcasm Detection: A Survey ACM Computer Surveys, Sep 2017.
• CASCADE: Contextual Sarcasm Detection in Online Discussion Forums 27th International Conference on Computational Linguistics, Aug 2018.
• A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks International Journal of Advanced Research in Computer Engineering & Technology, Volume 6, Issue 1, Jan 2017.
• Detecting Sarcasm with Deep Convolutional Neural Networks

• Naive Bayes and Text Classification. Oct 2014
• Bag of Tricks for Efficient Text Classification
• Text Classifier Algorithms in Machine Learning July 2017
• Classifying Documents in the Reuters-21578 R8 Dataset. August 2016.
• Tidy Text Mining Beer Reviews Jan 2018.
• Implementing a CNN for Text Classification in TensorFlow
• Using fastText and Comet.ml to classify relationships in Knowledge Graphs
• Multi-Class Text Classification with Scikit-Learn
• Machine Learning with Text in scikit-learn (PyCon 2016)
• How to solve 90% of NLP problems: a step-by-step guide

• Entity Extraction and Network Analysis. Python, StanfordCoreNLP
• NLP Techniques for Extracting Information

• Text Clustering: Get quick insights from Unstructured Data. July 2017.
• Document Clustering. MSc Thesis.
• Document Clustering: A Detailed Review. Shah and Mahajan. IJAIS 2012.
• Document Clsutering with Python A GitHub repository that clusters IMDB movie descriptions. Based on this original tutorial, whose GitHub repo is here.
• 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

• Topic models: Past, present, and future
• Word vectors using LSA, Part - 2
• Probabilistic Topic Models
• LEGO color themes as topic models Sep 2017.
• How our startup switched from Unsupervised LDA to Semi-Supervised GuidedLDA
• Topic Modeling with LSA, PLSA, LDA & lda2Vec Aug 2018.
• text2vec's Description of Topic Models
• Topic Modelling Portal
• Applications of Topic Models 2017.
• MACS 30500: Text analysis: topic modeling

• Sentiment analysis on Trump's tweets using Python. Sep 2017.
• Donald Trump vs Hillary Clinton: sentiment analysis on Twitter mentions
• Does sentiment analysis work? A tidy analysis of Yelp reviews
• CACM: Techniques and Applications for Sentiment Analysis
• Twitter mood predicts the stock market
• A nonlinear impact: evidences of causal effects of social media on market prices
• Stock Sentiment Data: Measuring the Mood of the Market
• Stock Prediction Using Twitter Sentiment Analysis. Stanford course project report.
• Forbes: How Quant Traders Use Sentiment To Get An Edge On The Market
• Sentdex: Quantifying the Qualitative
• Leveraging international market sentiment for trading strategies
• From tweets to polls: Linking text sentiment to public opinion time series
• Lexicon-Based Methods for Sentiment Analysis Uses SO-CAL (Semantic Orientation - CALculator, a measure of subjectivity and opinion for sentimental analysis
• On the negativity of negation
• Blog Post: That Sentimental Feeling
  • Update: More Syuzhet Validation
• Trump2Cash: A stock trading bot powered by Trump tweets
  • Related Blog Article: This Machine Turns Trump Tweets into Planned Parenthood Donations
• Unsupervised Sentiment Neuron. April 2017.
• Current State of Text Sentiment Analysis from Opinion to Emotion Mining Feb 2017
• Does sentiment analysis work? A tidy analysis of Yelp reviews
• 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 August 2015.
• Data Science 101: Sentiment Analysis in R Tutorial. October 2017.
• Cannes Lions 2017: Hungerithm, Mars Chocolate Australia (Clemenger BBDO, Melbourne). Snickers price goes down as anger goes up.
• A survey on sentiment analysis challenges. April 2016.
• Challenges in Sentiment Analysis. 2015.
• Sentiment Analysis Tools Overview, Part 1. Positive and Negative Words Databases. July 2017
• Sentiment analysis: 10 applications and 4 services
• Emotion and Sentiment Analysis: A Practitioner’s Guide to NLP
• Sentiment analysis: 10 applications and 4 services. June 2018.
• TWITTER SENTIMENT ANALYSIS USING COMBINED LSTM-CNN MODELS
• Breakthrough Research Papers and Models for Sentiment Analysis Article contains different types of models for Sentiment Analysis.

• Text Summarization with Gensim
• Unsupervised Text Summarization using Sentence Embeddings
• Improving Abstraction in Text Summarization Proposing two techniques for improvement
• Text Summarization and Categorization for Scientific and Health-Related Data

• Blog Post: Found in translation: More accurate, fluent sentences in Google Translate Nov 2016
• NYTimes: The Great A.I. Awakening Dec 2016
• Machine Learning Translation and the Google Translate Algorithm
• Neural Machine Translation (seq2seq) Tutorial
• Paper Dissected: “Attention is All You Need” Explained Explanation of an important paper that first introduced 'Attention mechanism' in 2017.
• The Annotated Transformer A line-by-line implementation of "Attention Is All You Need".
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding A new language representation model published in 2018. Implementation code. Pytorch port.
• Phrase-Based & Neural Unsupervised Machine Translation Proposed two model variants: neural and phrase-based model. Awarded as the Best Paper Award at EMNLP 2018. Implementation code.

• Meet Lucy: Creating a Chatbot Prototype
• Microsoft Bot Framework.
• Training Millions of Personalized Dialogue Agents
• Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot. 2016.
• A Survey on Dialogue Systems: Recent Advances and New Frontiers Jan 2018.
• Examining the Impact of an Automated Translation Chatbot on Online Collaborative Dialog for Incidental L2 Learning
• [Create a banking chatbot with FAQ discovery, anger detection and natural language understanding] (https://github.com/IBM/watson-banking-chatbot)

• agrep method in R. Approximate String Matching (Fuzzy Matching)
• fuzzywuzzy package in R. Example usage.
• Fuzzy String Matching – a survival skill to tackle unstructured information
• The RecordLinkage Package: Detecting Errors in Data
• R package fastLink: Fast Probabilistic Record Linkage
• Fuzzy merge in R
• Learning Text Similarity with Siamese Recurrent Networks
• Dedupe: A Python library for accurate and scaleable fuzzy matching, record deduplication and entity-resolution.
• recordlinkage: A toolkit for record linkage and deduplication written in Python.

• An Intuitive Understanding of Word Embeddings: From Count Vectors to Word2Vec
• An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation 2016. From IBM.
• Document Embedding with Paragraph Vectors 2015. From Google.
• GloVe Word Embeddings Demo 2017. From fasti.
• Text Classification With Word2Vec 2016.
• Document Embedding 2017
• From Word Embeddings To Document Distances 2015.
• Word Embeddings, Bias in ML, Why You Don't Like Math, & Why AI Needs You 2017. Rachel Thomas (fastai)
• Word Vectors in Natural Language Processing: Global Vectors (GloVe). Aug 2018.
• Doc2Vec Tutorial on the Lee Dataset
• Word Embeddings in Python with Spacy and Gensim
• The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning). Dec 2018.
• Deep Contextualized Word Represenations. ElMo. PyTorch implmentation. TF Implementation
• Universal Language Model Fine-tuning for Text Classification. Implementation code.
• Supervised Learning of Universal Sentence Representations from Natural Language Inference Data.
• Learned in Translation: Contextualized Word Vectors. CoVe.
• Distributed Representations of Sentences and Documents. Paragraph vectors. See doc2vec tutorial at gensim
• sense2vec. Word sense disambiguation.
• Skip Thought Vectors. Word representation method.
• Sequence to Sequence Learning with Neural Networks
• The Amazing Power of Word Vectors. 2016.
• Contextual String Embeddings for Sequence Labeling. 2018.
• A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks Introducing a multi-task learning approach for a set of interrelated NLP tasks. Presented at AAAI conference in January 2019.Implementation code.
• An Idiot’s Guide to Word2vec Natural Language Processing

• Understanding Convolutional Neural Networks for NLP.
  • Briefly describes convolutions, kernels, and CNNs (including hyperparameters and channels), then explains how CNNs apply to text data input using images as an analogue. Explains that within NLP, CNNs excel for classification tasks (e.g. sentiment analysis, spam classification).
• Keras LSTM tutorial – How to easily build a powerful deep learning language model.
  • First half of the article describes RNNs, the anatomy of an LSTM cell, LSTM networks. Second half is a walkthrough of features in Keras for LSTM implementation using generators for data input.
• Deep Learning for Natural Language Processing: Tutorials with Jupyter Notebooks.
  • A short article containing links and descriptions to further video tutorials for DL approaches to NLP problems. Five lessons total including preprocessing, word representations, and LSTM, among other topics.
• A Survey of the Usages of Deep Learning in Natural Language Processing.
  • A 35-page academic literature review of DL in NLP (University of Colorado, July 2018). Detailed description of neural network architectures followed by a comprehensive set of applications.
• Sequence Classification with Human Attention: Using human attention derived from eye-tracking corpora to regularize attention in recurrent neural networks (RNN). Implementation code.
• Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python
• WHAT EVERY NLP ENGINEER NEEDS TO KNOW ABOUT PRE-TRAINED LANGUAGE MODELS. 2019.

• Using fastText and Comet.ml to classify relationships in Knowledge Graphs
• WTF is a knowledge graph?

• SQuAD leaderboard. A list of the strongest-performing NLP models on the Stanford Question Answering Dataset (SQuAD).
  • SQuAD 1.0 paper (Last updated October 2016). SQuAD v1.1 includes over 100,000 question and answer pairs based on Wikipedia articles.
  • SQuAD 2.0 paper (October 2018). The second generation of SQuAD includes unanswerable questions that the NLP model must identify as being unanswerable from the training data.
• GLUE leaderboard.
  • GLUE paper (September 2018). A collection of nine NLP tasks including single-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).

• Udemy: Deep Learning and NLP A-Z™: How to create a ChatBot
• Udemy: Natural Language Processing with Deep Learning in Python
• Udemy: NLP - Natural Language Processing with Python
• Udemy: Deep Learning: Advanced NLP and RNNs
• Udemy: Natural Language Processing and Text Mining Without Coding

• Stanford CS 224N / Ling 284
  • Website: http://cs224d.stanford.edu/
  • Reddit: https://www.reddit.com/r/CS224d/comments/4n04ew/follow_along_with_cs224d_2015_or_2016/
• Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017)

• Courses for "natural language processing" on Coursera
• Coursera: Applied Text Mining in Python
• Coursera: Nartual Language Processing
• Coursera: Sequence Models for Time Series and Natural Language Processing
• Coursera: Coursera: Clinical Natural Language Processing

• DataCamp: Natural Language Processing Fundamentals in Python
• DataCamp: Sentiment Analysis in R: The Tidy Way
• DataCamp: Text Mining: Bag of Words
• DataCamp: Building Chatbots in Python

• Natural Language Processing | Dan Jurafsky, Christopher Manning
• Deep Learning for NLP. DeepMind and University of Oxford Department of Computer Science.
• CMU CS 11-747: Neural Network for NLP
• YSDA NLP course. Yandex School of data analysis.
• CMU Language and Statistics II: (More) Empirical Methods in Natural Language Processing
• UT CS 388: Natural Language Processing
• Columbia: COMS W4705: Natural Language Processing
• Columbia: COMS E6998: Machine Learning for Natural Language Processing (Spring 2012)
• Machine Translation: Spring 2016
• Commonlounge: Learn Natural Language Processing: From Beginner to Expert
• Big Data University: Advanced Text Analytics – Getting Results with SystemT
• Udacity: Natural Language Processing Nanodegree
• University of Michigan: Natural Language Processing

• R packages
  • tm: Text Mining.
  • lsa: Latent Semantic Analysis.
  • lda: Collapsed Gibbs Sampling Methods for Topic Models.
  • textir: Inverse Regression for Text Analysis.
  • corpora: Statistics and data sets for corpus frequency data.
  • tau: Text Analysis Utilities.
  • tidytext: Text mining using dplyr, ggplot2, and other tidy tools.
  • Sentiment140: Sentiment text analysis
  • sentimentr: Lexicon-based sentiment analysis.
  • cleanNLP: ML-based sentiment analysis.
  • RSentiment: Lexicon-based sentiment analysis. Contains support for negation detection and sarcasm.
  • text2vec: Fast and memory-friendly tools for text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), similarities.
  • fastTextR: Interface to the fastText library.
  • LDAvis: Interactive visualization of topic models.
  • keras: Interface to Keras, a high-level neural networks 'API'. (RStudio Blog: TensorFlow for R)
  • retweet: Client for accessing Twitter’s REST and stream APIs. (21 Recipes for Mining Twitter Data with rtweet)
  • topicmodels: Interface to the C code for Latent Dirichlet Allocation (LDA).
  • textmineR: Aid for text mining in R, with a syntax that should be familiar to experienced R users.
  • wordVectors: Creating and exploring word2vec and other word embedding models.
  • gtrendsR: Interface for retrieving and displaying the information returned online by Google Trends.
    • Analyzing Google Trends Data in R
  • textstem: Tools that stem and lemmatize text.
• Python modules
  • NLTK: Natural Language Toolkit.
    • Video: NLTK with Python 3 for Natural Language Processing
  • scikit-learn: Machine Learning in Python
    • Tutorial
  • spaCy: Industrial-Strength Natural Language Processing in Python.
  • textblob: Simplified Text processing.
    • Natural Language Basics with TextBlob
  • Gensim: Topic Modeling for humans.
  • textmining: Python Text Mining utilities.
  • Scrapy: Open source and collaborative framework for extracting the data you need from websites.
  • lda2vec: Tools for interpreting natural language.
  • PyText A deep-learning based NLP modeling framework built on PyTorch.
  • sent2vec: General purpose unsupervised sentence representations.
  • flair: A very simple framework for state-of-the-art Natural Language Processing (NLP)
  • word_forms: Accurately generate all possible forms of an English word e.g "election" --> "elect", "electoral", "electorate" etc.
  • AllenNLP: Open-source NLP research library, built on PyTorch.
  • BigARTM: Fast topic modeling platform.
  • Scattertext: Beautiful visualizations of how language differs among document types.
  • embeddings: Pretrained word embeddings in Python.
  • fastText: Library for efficient learning of word representations and sentence classification.
  • Google Seq2Seq: A general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summarization, Conversational Modeling, Image Captioning, and more.
  • polyglot: A natural language pipeline that supports multilingual applications.
  • textacy: NLP, before and after spaCy
• Apache Tika: a content analysis tookilt.
• HDF5: An open source file format that supports large, complex, heterogeneous data. Requires no configuration.
  • h5py: Python HDF5 package
• Stanford CoreNLP: a suite of core NLP tools
  • Also checkout http://corenlp.run for a hosted version of the CoreNLP server.
  • Introduction to StanfordNLP: An Incredible State-of-the-Art NLP Library for 53 Languages (with Python code)
• Stanford Parser
• Stanford POS Tagger
• Stanford Named Entity Recognizer
• Stanford Classifier
• Stanford OpenIE
• Stanford Topic Modeling Toolbox
• MALLET: MAchine Learning for LanguagE Toolkit
  • Github: https://github.com/mimno/Mallet
• Apache OpenNLP: Machine learning based toolkit for text NLP.
• Streamcrab: Real-Time, Twitter sentiment analyzer engine http:/www.streamcrab.com
• TextRazor API: Extract Meaning from your Text.
• fastText. Library for fast text representation and classification. Facebook.
• Comparison of Top 6 Python NLP Libraries.

• Systran - Enterprise Translation Products
• SAS Text Miner (Part of SAS Enterprise Miner)
• SAS Sentiment Analysis
• STATISTICA
  • Text Mining (Big Data, Unstructured Data)
• KNIME
• RapidMiner
• Gate
• IBM Watson
  • Video: How IBM Watson learns (3 minutes)
  • Video: IBM Watson on Jeapardy! (10 minutes)
  • Video: IBM Watson: The Science Behind an Answer (7 minutes)
• Crimson Hexagon
• Stocktwits: Tap into the Pulse of Markets
• Meltwater
• CrowdFlower: AI for your business.
• Lexalytics Sematria: API and Excel plugin.
• Rosette Text Analytics: AI for Human Language
• Google's Natural Language API: Derive insights from unstructured text using Google machine learning
• Alchemy API
• Monkey Learn
• LightTag Annotation Tool. Hosted annotation tool for teams.
• Anafora: Free and open source web-based raw text annotation tool
• brat: Rapid annotation tool.
• Amazon Lex: A service for building conversational interfaces into any application using voice and text.
• Google's Colab: Ready-to-go Notebook environment that makes it easy to get up and running.

• Apache PDFBox
• Tabula: A tool for liberating data tables locked inside PDF files.
• PDFLayoutTextStripper: Converts a pdf file into a text file while keeping the layout of the original pdf.
• pdftabextract: A set of tools for extracting tables from PDF files helping to do data mining on (OCR-processed) scanned documents.
• 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
• PyPDF2: PDF file manipulation (PDF to PDF).

• MIT OpenNPT for neural machine translation and neural sequence modeling
• Stemming & Lemmatization with Python NLTK
• Stanford Parser
• Stanford CoreNLP
• word2vec demo
• Another word2vec demo
• sense2vec: Semantic Analysis of the Reddit Hivemind
• RegexPal: Great tool for testing out regular expressions.
• AllenNLP Demo: Great demo using AllenNLP of everything from Named Entity Recognition to Textual Entailment.
• IBM NLP Demo: Examine a news article or other content from the link for NLP analysis using IBM NLP.

• UCI's Text Datasets
• data.world's Text Datasets
• Awesome Public Datasets' Natural Languge
• Insight Resources Datasets
• Bing Sentiment Analysis
• Consumer Complaint Database. From the Consumer Financial Protection Bureau.
• Sentiment Labelled Sentences Data Set . Contains sentences labelled as "positive" or "negative", from imdb.com, amazon.com, and yelp.com.
• Amazon product data
• Data is Plural
• FiveThirtyEight's datasets
• r/datasets
• Awesome public datasets
• R's datasets package
• 200,000 Russian Troll Tweets
• Wikipedia: List of datasets for ML research
• Google Dataset Search
• Kaggle: UMICH SI650 - Sentiment Classification
• Lee's Similarity Data Sets
• Corpus of Presidential Speeches (CoPS) and a Clinton/Trump Corpus
• 15 Best Chatbot Datasets for Machine Learning
• A Survey of Available Corpora for Building Data-Driven Dialogue Systems
• nlp-datasets
• Hate-speech-and-offensive-language
• First Quora Dataset Release: Question Pairs
• The Best 25 Datasets for Natural Language Processing
• SWAG: A large-scale dataset created for Natural Language Inference (NLI) with common-sense reasoning.
• MIMIC: an openly available dataset developed by the MIT Lab for Computational Physiology, comprising deidentified health data associated with ~40,000 critical care patients.
• Clinical NLP Dataset Repository: A curated list of publicly-available clinical datasets for use in NLP research.
• Million Song Lyrics

• MPQA Lexicon
• SentiWordNet
• AFINN
• Bing
• nrc
• vaderSentiment

• AskReddit: People with a mother tongue that isn't English, what are the most annoying things about the English language when you are trying to learn it?
• Funny Video: Emotional Spell Check
• How to win Kaggle competition based on NLP task, if you are not an NLP expert
• Detecting Gang-Involved Escalation on Social Media Using Context Detecting Aggression and Loss in social media using CNN
• Reasoning about Actions and State Changes by Injecting Commonsense Knowledge Incorporating global, commonsense constraints & biasing reading with preferences from large-scale corp
• Using Natural Language Processing for Automatic Detection of Plagiarism
• Probabilistic Graphical Models: Lagrangian Relaxation Algorithms for Natural Language Processing

• Deep Learning for NLP, advancements and trends in 2017
• 10 Exciting Ideas of 2018 in NLP

• awesome-nlp: A curated list of resources dedicated to Natural Language Processing (NLP)
• Deep Learning for NLP resources: State of the art resources for NLP sequence modeling tasks such as machine translation, image captioning, and dialog.
• Speech and Natural Language Processing
• Opinion Mining, Sentiment Analysis, and Opinion Spam Detection
• awesome-machine-learning
• Sentiment140: Allows you to discover the sentiment of a brand, product, or topic on Twitter.
• Awesome Deep Learning for Natural Language Processing (NLP)
• Paper with Code: A fantastic list of recent machine learning papers on ArXiv, with links to code.

Contributions are more than welcome! Please read the contribution guidelines first.

To the extent possible under law, @stepthom has waived all copyright and related or neighboring rights to this work.