Today, more data is accumulated than ever before. It has been estimated that over 80% of data collected by businesses is unstructured, mostly in the form of free text. The statistical community has developed many tools for analysing textual data, both in the areas of exploratory data analysis (e.g. clustering methods) and predictive analytics. In this talk, Philipp Burckhardt will discuss tools and libraries that you can use today to perform text mining with Node.js. Creative strategies to overcome the limitations of the V8 engine in the areas of high-performance and memory-intensive computing will be discussed. You will be introduced to how you can use Node.js streams to analyse text in real-time, how to leverage native add-ons for performance-intensive code and how to build command-line interfaces to process text directly from the terminal.

1. Text Mining with Node.js
Philipp Burckhardt
Carnegie Mellon University

2. Who am I?

3. Why re-invent the
wheel?

4. Reasons for using Node.js
• JavaScript - language of the Web
• Platform-agnostic (all operating systems, browser, CLIs and
desktop applications)
• V8 engine is fast enough to handle text mining tasks (faster than
Python or R)
• Core streams can handle real-time data & large amounts of
text
Drawback: Besides few popular packages like natural,
no eco-system of good text mining modules yet.

5. Use Case: deidentify

6. Use Case: deidentify
• Software for de-identification of protected health
information in free-text medical record data
• Developed as part of research project at CMU

7. The Challenge

8. Unstructured data
might account for
more than 80%
percent of data
collected.

9. Text Mining Overview

10. Typical Test Mining Tasks
• Sentiment
analysis

11. •Cluster analysis and
topic modeling: find
hidden patterns or
grouping in data

12. Getting practical
Sentiment Analysis of „State of the Union“ addresses
by President Obama

13. const getSpeeches = require( '@stdlib/datasets/sotu-addresses' );
const words = require( '@stdlib/datasets/afinn-111' );
const tm = require( 'text-miner' );
// Convert to a dictionary...
const len = words.length;
const dict = {};
for ( let i = 0; i < len; i++ ) {
dict[ words[i][0] ] = words[i][1];
}
const obamaSpeeches = getSpeeches({
'president': [ 'Barack Obama' ]
});
let obamaCorpus = new tm.Corpus(
obamaSpeeches.map( x => x.text )
)
.trim()
.toLower()
.removeInterpunctuation();
// Calculate sentiments...
const docs = obamaCorpus.getTexts();
const sentiments = [];
for ( let i = 0; i < docs; i++ ) {
const words = docs[ i ].split( ' ' );
let score = 0;
for ( let j = 0; j < words.length; j++ ) {
const val = dict[ words[ j ] ];
if ( val ) { score += val; }
}
sentiments.push( score );
}
Pre-Processing
sentiments = [ 69, 47, 266, 75, 234, 234, 163, 157 ]

15. Topic Modeling
Goal: find documents which share the same themes
(e.g. politics, business, sports)

16. Latent Dirichlet Allocation
• Probabilistic model for text documents by Blei et
al.
• Documents are assumed to have a distribution
over topics
• Very popular because of its expandability

17. const getSpeeches = require( '@stdlib/datasets/sotu-addresses' )
const lda = require( '@stdlib/nlp/latent-dirichlet-allocation' );
const tm = require( 'text-miner' );
let speeches = getSpeeches({ range: [ 1930, 2010 ] })
.map( ( e ) => e.text );
let corpus = new tm.Corpus( speeches );
corpus = corpus
.toLower()
.removeInterpunctuation()
.removeWords( tm.STOPWORDS.EN );
let docs = corpus.getTexts();
let model = lda( docs, 3 );
model.fit( 1000, 100, 10 );
lda( <Documents Array>, <Number of Topics> )
model.fit( <Iterations>, <Burnin>, <Thinning> )

18. Results for SOTU addresses
from 1930 to 2010
Topic Words
1 world, peace, war, nations, free,
people, great, nation, united,
freedom, power, military,
american, men, defense, time,
forces, strength
2 america, people, american, years,
americans, year, work, make,
children, congress, tonight, time,
tax, country, government, health,
budget, care
3 government, year, federal,
program, congress, economic,
states, national, administration,
million, policy, public, dollars,
legislation, programs, billion,
system, years, united, fiscal

19. Text Analysis using the
Command Line

20. Rationale
• Data pipelines using UNIX shell commands
• Processing of shell commands is done in parallel
• Memory usage
• V8 engine has default limit of 1.76 GB on 64 bit machine
(changeable via --max_old_space_size=<size>)
• Use stream processing instead of batch processing to avoid
high memory usage

21. LIVE DEMONSTRATION