cseHdz / text_mining_resources

Resources for learning about Text Mining and Natural Language Processing

Text Mining and Natural Language Processing Resources

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

Table of Contents

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

Books

R

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

Python

• 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

General

• 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)

Blogs

• 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

Blog Articles, Papers, Case Studies

General

• 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)
• a gentle introduction to historical data analysis
• 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.
• A Tidytext Analysis of the Weinstein Effect Dec 2013.
• Comparison of the Most Useful Text Processing APIs

Scraping

• 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.

Parsing

• Deep Parsing in Watson

Cleaning

• How to solve 90% of NLP problems: a step-by-step guide Jan 2018.

Stemming

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

Dimensionality Reduction

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

Sarcasm Detection

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

Document Classification

• 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

• Entity Extraction and Network Analysis. Python, StanfordCoreNLP

Document Clustering and Document Similarity

• 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

Concept Analysis/Topic Modeling

• 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

• 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.
• 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

Text Summarization

• Text Summarization with Gensim
• Unsupervised Text Summarization using Sentence Embeddings

Machine Translation

• 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

Q&A Systems, Chatbots

• Meet Lucy: Creating a Chatbot Prototype
• Microsoft Bot Framework. A YouTube video describing the product.
• Training Millions of Personalized Dialogue Agents
• Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot. 2016.

Fuzzy Matching, Probabilistic Matching, Etc.

• 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

Word and Document Embeddings

• 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.
• 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.

Deep Learning

• Understanding Convolutional Neural Networks for NLP
• 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

Knowledge Graphs

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

Online courses

• Stanford CS 224N / Ling 284
• 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.
• 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/
• 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
• 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
• Coursera: Introduction to Natural Language Processing
• Coursera: Nartual Language Processing
• Commonlounge: Learn Natural Language Processing: From Beginner to Expert
• Udacity: Natural Language Processing Nanodegree

APIs and Libraries

• 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.
• 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: 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.

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.

Getting Data out of PDFs

• 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

Online Demos and Tools

• Peter Holme's word stemmer
• Stanford Parser
• Stanford CoreNLP
• word2vec demo
• Another word2vec demo

Datasets

• 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

Lexicons for Sentiment Analysis

• MPQA Lexicon
• SentiWordNet
• AFINN
• Bing
• nrc

Misc

• 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

Meta

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

Other Curated Lists

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

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License

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