A primer on text mining for business

"Text mining" refers to using computational methods to find interesting information in texts.

Quasi synonyms:

→ "Natural language" insists on the fact that we deal with "human language" (possibly with slang, grammar and spelling errors, etc.)

→ not "programming language"

→ this expression shows that of course, text mining is practised by computer scientists but linguists as well. Some approaches to text mining put a heavy emphasis on grammatical notions (structure of sentences etc.), while other methods ignore grammar and syntax completly and still get good results.

A "corpus" is a collection of documents. The plural form exists: corpora. So data scientists doing text mining refer to their "dataset" or "corpus".

The list above is to convey that "text" is virtually everywhere, and as obvious as it seems we should not forget about this diversity of sources.

Sentiment analysis, also called opinion mining or sentiment detection.

Is this piece of text of a positive or negative tone? This type of text mining is useful to filter a large number of documents and retain only those that need action (eg, addressing negative comments).

Companies are typically interested in sentiment analysis in conjuction with topic modeling: they need to identify which topic elates negative sentiments.

For a review of methods for sentiment analysis, see this review article.

This type of tool aims at discovering the main topic or the multiple topics of a corpus. This is an essential function for:

(want to know more about topic modelling? Again, the digital humanities are a good starting point. Check there, there or there.)

How can a software "understand" the difference between "Paris Hilton" and "Hilton Hotel, in Paris"?

Named Entity Recognition (NER) consists in matching an expression with the proper definition, given the context of use. Clever techniques and data sources (like DBPedia) are used to achieve good results.

Semantic disambiguation or "Word sense disambiguation" consists in finding the meaning of an expression when multiple meanings are possible.

See Google translate but also the Skype Translator.

Shortening a text while keeping its core message intact. See this reference for a short, technical review.

A demo for Named Entity Recognition (the demo is for a service delivered as an API):

https://dandelion.eu/semantic-text/entity-extraction-demo/

On the website above, try first:

Then:

Visit this page and try writing a sentence with a negative or positive emotion / sentiment:

http://nlp.stanford.edu:8080/sentiment/rntnDemo.html

http://translate.google.com

The current state of text mining makes it easy to profile individuals, based on the texts they write on social networks.

Without text mining, we have access to “external”, “cold” states of the individual:

With text mining, there is access to “internal”, “hot” cognitive states of individuals:

See these following studies:

“Predicting the Political Alignment of Twitter Users” by Conover et al. (2011).

“Political Tendency Identification in Twitter using Sentiment Analysis Techniques” by Pla and Hurtado (2014).

“Private traits and attributes are predictable from digital records of human behavior” by Kosinski et al. (2013).

See this article in the New York Times examining the role of Cambridge Analytica in profiling voters at the service of Donal Trump’s campaign in 2016:

https://www.nytimes.com/2017/03/06/us/politics/cambridge-analytica.html

These text mining techniques get even more precise when mixed with network analysis and machine learning.

Printed text is typically harder and slower to analyze, because it needs to be scanned first (the technical term is OCR). The process of OCR introduces errors.

Check this paper in the Digital Humanities Quarterly for a deeper look into this issue, in the context of historical research.

And even when the text is in a digital form, it can be hard to use: extracting text from a pdf is not trivial at all, and this is part of the toolchain of data science.

Many operations in text mining will break when the language changes.

For example, the German language capitalizes nouns. It can be confusing to an algorithm trained on a corpus in English where only names are capitalized: simple nouns could be tagged as first names or family names.

This is just one of many examples. Text mining applications often break, are less efficient and / or are more costly when they handle multiple languages.

Text mining applications will have a relatively easy time on text published by Reuters news, because it is written in a formal style.

It will have a harder time on a Facebook message written by a teenager, peppered with slang, emojis and spelling shortcuts.

This project tries to crack the challenge of detecting irony in short texts: http://deepmoji.mit.edu/

Not working perfectly. Irony is hard because it needs contextual knowledge to guess that the real meaning is different from the literal meaning.

Translation remains very imperfect.

Translation has been mostly solved for the most spoken languages.

IBM Watson is a software which beat human players at the TV Game "Geopardy" (and that was in 2011)

Yet, mining text to produce new "reasoning" in general situations by machines has not made much progress yet until 2019.

In 2019, language models using self supervised learning achieved unheard levels of precision for the generation of text.

You can try it yourself here:

Tokenization is finding terms in a sentence. For example, "I am Dutch" is tokenized into "I", "am", "Dutch".

Trivial? Not so much. Try tokenizing a sentence in Mandarin!

With stemming, “liked” and “like” will be reduced to their stem “lik” to facilitate further operations

With lemmatizing, “liked”, “like” and “likes” will be grouped to count them as one basic semantic unit

POS detects the grammatical function of the terms used in a sentence, to facilitate translation or other tasks.

See for example the online demo by the Stanford team shown above: POS tagging is used to decompose the sentence.

“Starting the text analysis with a bag-of-words model” means just listing and counting all different words in the text, without caring for the sequence of the words in the text, as a first approach.

The text “I am Dutch” is made of 3 words: I, am, Dutch.

But it can also be interesting to look at bigrams in the text: “I am”, “am Dutch”. Or trigrams: “I am Dutch”.

N-Grams is the general approach of considering groups of n terms in a document.

This can reveal interesting things about frequent expressions used in the text.

A good example of how useful n-grams can be: visit the Ngram Viewer by Google: https://books.google.com/ngrams

Three types of use:

Example: getting a view on the activity of a business school through a map of its scientific publications.

Find references for this lesson, and other lessons, here.

This course is developed by Clement Levallois.

Discover my other courses in data / tech for business: https://www.clementlevallois.net

Or get in touch via Twitter: seinecle m/seinecle[seinecle] m/seinecle[seinecle] m/seinecle[seinecle] m/seinecle[seinecle] m/seinecle[seinecle] m/seinecle[seinecle] m/seinecle[seinecle] m/seinecle[seinecle] m/seinecle[seinecle] m/seinecle[seinecle] /www.twitter.com/seinecle[seinecle] m/seinecle[seinecle] m/seinecle[seinecle] m/seinecle[seinecle]