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

• Other Curated Lists
• Books
• Blogs, Articles, Papers, Case Studies
• Online Courses
• APIs and Libraries
• Products
• Online Tools
• Datasets
• Misc
• Meta

• awesome-nlp
• Deep Learning for NLP resources
• Speech and Natural Language Processing
• Opinion Mining, Sentiment Analysis, and Opinion Spam Detection
• awesome-machine-learning
• Sentiment140

• Taming Text
• Natural Language Processing with Python
• 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
• Text Mining with R
• Mastering Text Mining with R

• 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?
• Lekta Blog
• 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)
• a gentle introduction to historical data analysis
• Why Text Mining May Be The Next Big Thing. March 2012.
• BREAKTHROUGH ANALYSIS. A blog by Seth Grimes, on NLP, text analytics, sentiment analysis, BI, visualization and more.
• 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.
• A Tidytext Analysis of the Weinstein Effect Dec 2013.

• https://blog.scrapinghub.com/2016/01/19/scrapy-tips-from-the-pros-part-1/
• Extract text from any document; no muss, no fuss.. July 2014.
• Using Scrapy to Build your Own Dataset Sep 2017.

• Deep Parsing in Watson

• Article: Text Stemming: Approaches, Applications, and Challenges

• Automatic Sarcasm Detection: A Survey ACM Computer Surveys, Sep 2017.

• 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.

• Entity Extraction and Network Analysis. Python, StanfordCoreNLP

• Text Clustering: Get quick insights from Unstructured Data. July 2017.
• Document Clustering. MSc Thesis.

• 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

• 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
• News Sentiment Analysis Using R to Predict Stock Market Trends. SMU lecture.
• On the Predictability of Stock Market Behavior using StockTwits Sentiment and Posting Volume
• 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
• 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.

• Text Summarization with Gensim

• 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

• Meet Lucy: Creating a Chatbot Prototype

• 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

• An Intuitive Understanding of Word Embeddings: From Count Vectors to Word2Vec

• DataCamp: Natural Language Processing Fundamentals in Python
• DataCamp: Sentiment Analysis in R: The Tidy Way
• DataCamp: Text Mining: Bag of Words
• Coursera: Introduction to Natural Language Processing
• Stanford course on NLP: Dan Jurafsky and Chris Manning
• Stanford Deep Learning NLP Course
  • Website: http://cs224d.stanford.edu/
  • Reddit: https://www.reddit.com/r/CS224d/comments/4n04ew/follow_along_with_cs224d_2015_or_2016/
• Coursera: Nartual Language Processing
• Stanford CS 224N / Ling 284
• CMU Language and Statistics II: (More) Empirical Methods in Natural Language Processing
• UT CS 388: Natural Language Processing
• Coursera: Applied Text Mining in Python
• Big Data University: Text Analytics – Getting Results with SystemT
• Big Data University: Advanced Text Analytics – Getting Results with SystemT
• Big Data University: Text mining in action: Analyzing Twitter data for Democratic General Elections (BETA Version)
• Columbia: COMS W4705: Natural Language Processing
• Columbia: COMS E6998: Machine Learning for Natural Language Processing (Spring 2012)
• Machine Translation: Spring 2016

• 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: R package for sentiment text analysis
  • sentimentr Lexicon-based sentiment analysis.
  • cleanNLP ML-based sentiment analysis.
  • RSentiment Lexicon-based sentiment analysis. Contains support for negation detection and sarcasum.
• Python modules
  • NLTK: Natural Language Toolkit.
    • Video: NLTK with Python 3 for Natural Language Processing
  • 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.
  • gtrendsR
    • Analyzing Google Trends Data in R
  • Scrapy
• Apache Tika: a content analysis tookilt.
• Stanford CoreNLP: a suite of core NLP tools
  • Also checkout http://corenlp.run for a hosted version of the CoreNLP server.
• 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
• 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.

• 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

• 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

• Peter Holme's word stemmer
• Stanford Parser
• Stanford CoreNLP

• 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 lableed as "positive" or "negative", from imdb.com, amazon.com, and yelp.com.]
• Amazon product data

• MPQA Lexicon
• SentiWordNet
• AFINN
• Bing
• nrc

• 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

• Deep Learning for NLP, advancements and trends in 2017