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A Primer on Text Mining for Business

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Clement Levallois
Clement Levallois

Slides of the course on big data by C. Levallois from EMLYON Business School. For business students. Check the online video connected with these slides. -> Definition of text mining, the main categories of tools available (such as topic categorization or sentiment analysis) and their use for business.

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MK99 – Big Data 1 Big data & cross-platform analytics MOOC lectures Pr. Clement Levallois
MK99 – Big Data 2 A primer on text mining for business • Text mining: computational methods to find interesting information in texts • Quasi synonyms: – natural language processing (abbreviated in NLP) – computational linguistics (name of a scientific discipline)
MK99 – Big Data 3 Text… what kinds? • Books • Tweets • Product reviews on Amazon • LinkedIn profiles • The whole Wikipedia • Free text answers in the results of a survey • Tenders, contracts, laws, … • Print and online media • Archival material • …
MK99 – Big Data 4 What can be done? • Sentiment analysis – Is this piece of text of a positive or negative tone? • Topic modeling / topic detection – What is the main theme of this 20-page booklet? • Semantic disambiguation – “Paris” is mentioned in this text. Is this Paris Hilton or Paris, France? • Named Entity Recognition (NER) – Automatically find the individuals, organizations and events named in the text, and the relations between them. • Semantic enrichment – If you searched Google for “TV”, results for “television” will also show up • Language detection – “Ich spreche Deutsch” -> this sentence is written in German • Automatic Translation – See Google Translate •Summarizing –Shortening a text while keeping its core message intact •Spelling correction –Well, that’s easy •Topic Classification –Is this email a spam or not?
MK99 – Big Data 5 Amaze me! • Demo on sentiment analysis With a tool by Stanford: http://nlp.stanford.edu:8080/sentiment/rntnDemo.html • Demo on semantic disambiguation With a tool by a collaborative effort: http://dbpedia-spotlight.github.io/demo/ (click on “annotate”, and also change the text for one of your own)
MK99 – Big Data 6 What can’t be done yet (but is actively researched) • Detection of irony • Robust translation • Reasoning beyond Q&A What makes things harder • Non English texts • Slang and colloquial speech-forms • Real time processing
MK99 – Big Data 7 Example of routine operations when working with text (or, how to follow the most basic conversation in comput. linguistics) • Stemming – “liked” and “like” will be reduced to their stem “lik” to facilitate further operations • Lemmatizing – Grouping “liked”, “like” and “likes” to count them as one basic semantic unit • Part-of-Speech tagging (aka POS tagging) – Automatically detecting the grammatical function of the terms used in a sentence, to facilitate translation or else • “Starting the text analysis with a bag-of-words model” – Operation which consists in just listing and counting all different words in the text. • N-grams – 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”. – When neighboring words are considered together just like we did, they are called n-grams. This can reveal interesting things about frequent expressions used in the text. – A good example of how useful this can be: visit the Ngram Viewer by Google: https://books.google.com/ngrams
MK99 – Big Data 8 Chief benefit: Getting to know individuals better • Without text mining, we have access to “external”, “cold” states of the individual – Behavior (eg, clicks), external attributes (address, gender, encyclopedia entry), social networks (but relatively cold ones.) • With text mining, we have access to “internal”, “hot” states: - opinions - intentions - preferences - degree of consensus - social networks (who mentions whom: how, in which context) - implicit attributes of the speaker
MK99 – Big Data 9 How easy is it? • Too easy… the limit is legal and ethical, not technical “Predicting the Political Alignment of Twitter Users” by Conover et al. (2011). http://cnets.indiana.edu/wp-content/uploads/conover_prediction_socialcom_pdfexpress_ok_version.pdf “Political Tendency Identification in Twitter using Sentiment Analysis Techniques” by Pla and Hurtado (2014). http://anthology.aclweb.org/C/C14/C14-1019.pdf “Private traits and attributes are predictable from digital records of human behavior” by Kosinski et al. (2013). http://www.pnas.org/content/110/15/5802.abstract (and this gets even more powerful when mixing text mining, network analysis and machine learning)
MK99 – Big Data 10 What use for text mining in a business context? 1. Client facing 2. Business management 3. Business development
MK99 – Big Data 11 1. Market facing activities • Refined scoring: propensity scores (including churn), scoring of prospects •Refined individualization of campaigns –ads, email campaigns, coupons, etc. •Better community management –Getting a clear and precise picture of how customers and prospects perceive, talk about, and engage with your brand / product / industry.
MK99 – Big Data 12 2. Business Management • Organizational mapping – Getting a view of the organization through text flows. – Example: getting a view on the activity of a business school through a map of its scientific publications. • HRM – Finding talents in niche industries, based on the mining of their profiles • Marketing research – refined segmentation + targeting + positioning, measuring customer satisfaction, perceptual mapping.
MK99 – Big Data 13 3. Business development • Developing adjunct services – product recommendation systems (eg, Amazon’s) – detection and matching of needs (eg, detection of complaints / mood changes) – product enhancements (eg, content enrichment through localization/personalization) • Developing new products entirely, based on – different search engines – alert systems / automated systems based on monitoring textual input – knowledge databases – new forms of content curation / high value info creation + delivery
MK99 – Big Data 14 Interesting players through their “Data Services” package + many APIs listed on www.programmableweb.com
MK99 – Big Data 15 This slide presentation is part of a course offered by EMLYON Business School (www.em-lyon.com) Contact Clement Levallois (levallois [at] em-lyon.com) for more information.

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A Primer on Text Mining for Business

  • 1. MK99 – Big Data 1 Big data & cross-platform analytics MOOC lectures Pr. Clement Levallois
  • 2. MK99 – Big Data 2 A primer on text mining for business • Text mining: computational methods to find interesting information in texts • Quasi synonyms: – natural language processing (abbreviated in NLP) – computational linguistics (name of a scientific discipline)
  • 3. MK99 – Big Data 3 Text… what kinds? • Books • Tweets • Product reviews on Amazon • LinkedIn profiles • The whole Wikipedia • Free text answers in the results of a survey • Tenders, contracts, laws, … • Print and online media • Archival material • …
  • 4. MK99 – Big Data 4 What can be done? • Sentiment analysis – Is this piece of text of a positive or negative tone? • Topic modeling / topic detection – What is the main theme of this 20-page booklet? • Semantic disambiguation – “Paris” is mentioned in this text. Is this Paris Hilton or Paris, France? • Named Entity Recognition (NER) – Automatically find the individuals, organizations and events named in the text, and the relations between them. • Semantic enrichment – If you searched Google for “TV”, results for “television” will also show up • Language detection – “Ich spreche Deutsch” -> this sentence is written in German • Automatic Translation – See Google Translate •Summarizing –Shortening a text while keeping its core message intact •Spelling correction –Well, that’s easy •Topic Classification –Is this email a spam or not?
  • 5. MK99 – Big Data 5 Amaze me! • Demo on sentiment analysis With a tool by Stanford: http://nlp.stanford.edu:8080/sentiment/rntnDemo.html • Demo on semantic disambiguation With a tool by a collaborative effort: http://dbpedia-spotlight.github.io/demo/ (click on “annotate”, and also change the text for one of your own)
  • 6. MK99 – Big Data 6 What can’t be done yet (but is actively researched) • Detection of irony • Robust translation • Reasoning beyond Q&A What makes things harder • Non English texts • Slang and colloquial speech-forms • Real time processing
  • 7. MK99 – Big Data 7 Example of routine operations when working with text (or, how to follow the most basic conversation in comput. linguistics) • Stemming – “liked” and “like” will be reduced to their stem “lik” to facilitate further operations • Lemmatizing – Grouping “liked”, “like” and “likes” to count them as one basic semantic unit • Part-of-Speech tagging (aka POS tagging) – Automatically detecting the grammatical function of the terms used in a sentence, to facilitate translation or else • “Starting the text analysis with a bag-of-words model” – Operation which consists in just listing and counting all different words in the text. • N-grams – 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”. – When neighboring words are considered together just like we did, they are called n-grams. This can reveal interesting things about frequent expressions used in the text. – A good example of how useful this can be: visit the Ngram Viewer by Google: https://books.google.com/ngrams
  • 8. MK99 – Big Data 8 Chief benefit: Getting to know individuals better • Without text mining, we have access to “external”, “cold” states of the individual – Behavior (eg, clicks), external attributes (address, gender, encyclopedia entry), social networks (but relatively cold ones.) • With text mining, we have access to “internal”, “hot” states: - opinions - intentions - preferences - degree of consensus - social networks (who mentions whom: how, in which context) - implicit attributes of the speaker
  • 9. MK99 – Big Data 9 How easy is it? • Too easy… the limit is legal and ethical, not technical “Predicting the Political Alignment of Twitter Users” by Conover et al. (2011). http://cnets.indiana.edu/wp-content/uploads/conover_prediction_socialcom_pdfexpress_ok_version.pdf “Political Tendency Identification in Twitter using Sentiment Analysis Techniques” by Pla and Hurtado (2014). http://anthology.aclweb.org/C/C14/C14-1019.pdf “Private traits and attributes are predictable from digital records of human behavior” by Kosinski et al. (2013). http://www.pnas.org/content/110/15/5802.abstract (and this gets even more powerful when mixing text mining, network analysis and machine learning)
  • 10. MK99 – Big Data 10 What use for text mining in a business context? 1. Client facing 2. Business management 3. Business development
  • 11. MK99 – Big Data 11 1. Market facing activities • Refined scoring: propensity scores (including churn), scoring of prospects •Refined individualization of campaigns –ads, email campaigns, coupons, etc. •Better community management –Getting a clear and precise picture of how customers and prospects perceive, talk about, and engage with your brand / product / industry.
  • 12. MK99 – Big Data 12 2. Business Management • Organizational mapping – Getting a view of the organization through text flows. – Example: getting a view on the activity of a business school through a map of its scientific publications. • HRM – Finding talents in niche industries, based on the mining of their profiles • Marketing research – refined segmentation + targeting + positioning, measuring customer satisfaction, perceptual mapping.
  • 13. MK99 – Big Data 13 3. Business development • Developing adjunct services – product recommendation systems (eg, Amazon’s) – detection and matching of needs (eg, detection of complaints / mood changes) – product enhancements (eg, content enrichment through localization/personalization) • Developing new products entirely, based on – different search engines – alert systems / automated systems based on monitoring textual input – knowledge databases – new forms of content curation / high value info creation + delivery
  • 14. MK99 – Big Data 14 Interesting players through their “Data Services” package + many APIs listed on www.programmableweb.com
  • 15. MK99 – Big Data 15 This slide presentation is part of a course offered by EMLYON Business School (www.em-lyon.com) Contact Clement Levallois (levallois [at] em-lyon.com) for more information.