Sentiment Analysis/NLPMichael HenniganBrian O’BrienLesley Anne Quinn

Brian O’BrienLesley Anne Walker QuinnMichael Hennigan

[Garlitos, K. (2013)], [Tilus, G. (2013)], [Facebook (2013)] [Rafter, M.V. (2011)]

INTRODUCTION

HistoryRelate NLP to Sentiment Analysis and Big DataHow it applies to in BusinessKey PlayersHighlight some benefits, difficulties and success

NLP Goals• Paraphrasing• Translating• Question answering• Drawing inferences

Divisions• Language Processing (reader/listener)• Language Generation

(writer/speaker)• Language Understanding

Development ofNatural Language Processing

Adapted from Liddy (2001)

Four approaches to NLP

Levels of Language

(Liddy, 2001)

Phonology Morphology Lexical Syntactic

Semantic Discourse Pragmatic

Process Steps

Application of rules/data in

system

Rule/data construction,

Data analysis/model

buildingData collection

What is NLP (Natural Language Processing)?

Liddy (2001) states that Natural Language Processing is concerned with developing mechanisms (i.e. algorithms and other mathematical models) that enable computers to process and understand human language.

Computers generally process information in the form of numbers.

[ClipDealer (2009)]

[Liddy, E. D. (2001). Natural language processing.]

Where do we start?

Human language is extremely complex. We must find a way of breaking it down so that it can be processed by a computer.

Liu (2010) states that textual data can be identified under two categories: Facts and Opinions.

“Facts are objective expressions about entities, events and their properties. Opinions are usually subjective expressions that describe people’s sentiments, appraisals or feelings toward entities, events and their properties.”

[Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of natural language processing, 2, 627-666.]

Previously, natural language processing was very much focused on obtaining information from facts.

Facts are much easier to classify and quantify than opinions.

» Consider:“That car has a low fuel consumption.” (Fact)“That car has a high fuel consumption.” (Fact)“I think that car uses too much petrol.” (Opinion)

But recently, a new application of NLP has emerged, called Sentiment Analysis.

Sentiment Analysis involves analysing text in order to measure thePositive or Negative sentiment of people’s Opinions.

[Smith, I.]

SENTIMENT ANALYSIS

[Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of natural language processing, 2, 627-666.]

For example:

“I love my new iPhone! The picture quality is amazing!” (24/02/2014)

JoeBloggs92 says:

Object (Oj): iPhone

Feature (Fjk): picture quality

Sentiment Value (SOijkl): amazing (Positive Sentiment)

Opinion Holder (Hi): JoeBloggs92

Time (Ti): 24/02/2014

The five elements of the quintuple can now be sent to a database of iPhone customer opinions from where the data can be statistically analysed in order to produce meaningful insights.

[Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of natural language processing, 2, 627-666.]

Sentiment Analysis & Big Data

What is ‘Big Data’? In business, people often simply associate the term ‘Big Data’ with the field

of Data & Analytics

However, McAfee and Brynjolfsson (2012) identify 3 important factors which differentiate ‘Big Data.’

Volume:“Walmart collects more than 2.5 petabytes of data every hour from its customer transactions. A petabyte is the equivalent of about 20 million filing cabinets’ worth of text.”

[Roberts, H.A.]

Velocity:Big Data is constantly expanding at a rapid speed of growth.

Variety:“Large amounts of data now exist on virtually any topic of interest to a business. Each of us is now a walking data generator.”

[McAfee, A., & Brynjolfsson, E. (2012). Big data: the management revolution.Harvard business review, 90(10), 60-68.]

Big Data provides a Huge depository of information that businesses can tap in to.

Sentiment Analysis is one of a number of processes that have been developed in order to create value for businesses from these massive data sets.

Take the example of Twitter:

“There are 600,000 daily users of Twitter in Ireland.”

“We send, on average, 1 million tweets each day.”

[Tilus, G. (2013) ]

You could ask a marketing employee to analyse 50 tweets about the company’s product.But could you ask them to analyse 1,000 tweets? NO!We need computers to process this amount of information.

[O’Leary, S. (2013). The IrishDigital Consumer Report 2013.]

How does Sentiment Analysis relate to Big Data?

1. Flame Detection (Bad rants)

[‘Sam Mac’. (2014)]

2. New Product Perception

[‘Allan Ziskey’. (2013).]

How businesses can use Sentiment Analysis

3. Brand Perception

[The Irish Times. (2014).]

4. Reputation Management

[Jenkins, C.J. (2009). How Sentiment Analysis Works. Slideshare. Available: http://www.slideshare.net/mcjenkins/how-sentiment-analysis-works]

Example of customer review on travel website:

I loved this hotel. The lobby and pool areas were brilliant, the staff was on top of their game, and of course it was sparkling

clean.

The only issues were that the beds were too soft, the pool a bit cold, and there were children running

up and down the hallways at night.

(Wollan et al, 2010)

Text mining

Advanced text mining links

Noisiness of Rooms

I loved this hotel. The lobby and pool areas were brilliant, the staff was on top of their game, and of course it was sparkling clean. The only issues were that the beds were too soft, the pool a bit cold, and there were children running up and down the hallways at night.

NLP & Sentiment Analysis

I loved this hotel. The lobby and pool areas were brilliant, the staff was on top of their game, and of course it was sparkling clean. The only issues were that the beds were too soft, the pool a bit cold, and there were children running up and down the hallways at night.

Cold + Pool = -VE

NLP & Sentiment Analysis

Cold + Rooms = -ve

Too Soft + beds = -ve

I loved this hotel. The lobby and pool areas were brilliant, the staff was on top of their game, and of course it was sparkling clean. The only issues were that the beds were too soft, the pool a bit cold, and there were children running up and down the hallways at night.

NLP & Sentiment Analysis

Cold + Rooms = -ve

Too Soft + beds = -ve

Noisiness of rooms + Children running up and

down the hallways at night = -ve

I loved this hotel. The lobby and pool areas were brilliant, the staff was on top of their game, and of course it was sparkling clean. The only issues were that the beds were too soft, the pool a bit cold, and there were children running up and down the hallways at night.

Such advances in categorization and sentiment analysis have drastically reduced the time and cost to process and analyze the key conclusions from massive amounts of qualitative, unstructured customer feedback.

Companies now can draw deep insights on customers at a rate never before possible by quantifying the qualitative. (Wollan et al, 2010)

(Sas.com,2014)

Take a look at some workbenches…

(Sas.com,2014)

(Sas.com,2014)

(Halper, Kaufman and Kirsh, 2013)

Key Players

Key Players in Sentiment AnalysisRadian6: Software that allows businesses to monitor and join in on conversations across social media. User searches show results from over 150 million websites including

news, blogs, forums, Twitter, Facebook etc. Provides various options for analysis.

[SalesForce Radian6 (2012). Marketing Cloud Radian6 Introduction]

SAS (Statistical Analysis System):

Began as a university project to analyse agricultural research.

Founded in 1976 to help various types of customers, as demand for such software grew.

Offers Sentiment Analysis software similar to Radian6 among, as well as many other products.

[SAS Institute Inc]

IBM:

One of the largest and most profitable companies in the world.

Providers of technology, software, consultancy and other services.

Offers software similar to that of Radian6 and SAS, called ‘IBM Social Media Analytics.’

[IBM]

SentsCheck - Analysing social sentiment

Difficulties of NLP and Sentiment Analysis• NLP methodologies and techniques assume that the patterns

in grammar and the conceptual relationships between words in language can be articulated scientifically. (Sevilla, 2006)

(Shawn, 2014)

Difficulties in processing Sentiment Analysis • Complexity – 100’s of emoticons need to defined and

interpreted as to there meaning when used in different circumstances

• Colloquial language or local Slang – Consistently adding world wide slang words that are impossible to keep up to date with and interpret.

• Sarcasm - How a grouping of particular words which can be individually recognised as a positive can be collectively understood to have the opposite meaning i.e. “I cant wait to see that film again!”

Benefits to NLP and Sentiment Analysis to Business’

• Brand awareness- Company being spoken about, what’s the sentiment behind what is being said, does action need to be taken based on this positive or negative feed back.

• Product innovation & product extension-allows up to date insights on consumer wants and needs. When you think of the technology industry it moves at such a fast pace that conventional research surveys are as good as useless when developing your product line.

• Current trends-NLP gives an understanding of current market trends that allows companies to understand not just what the consumer perceives them to be doing wrong but also the consumers perception of their competitors.

• This data can be manipulated for use in any industry Political - anticipating voting patterns Agriculture - Airline industry - during times of disaster (general publics fears)

(Grimes, 2011)

Market Psych

Market Psych

Online financial news Financial social media Corporate interviews

(Grimes, 2011)(Davidson, 2012)(icopyright,2014)(king, 2014)

Past implementation successMarket Psych

• This was to enable them to predict future stock movement’s• 75% of Market Psych’s strategy is based on sentiment.(King, 2014)

(Unknown, 2014)

(Burkeman, 2012)

Past implementation successMarket Psych• 2009 World Health Organisation declared a public health

emergency due to H1N1 swine flu.

• American Airlines investors display signs of concern towards a possible reduction in airline travel.

• Market psych identified an opportunity.

• 2 days later Market Psych bought up all available stocks and sold them 6 days later at a 24% gain.

(King, 2014)

SummaryNLP – Allowing computers to process human language

Big Data - Volume, Velocity and Variety

Sentiment Analysis – Putting Big Data to use

How can Sentiment Analysis be used?

Flame Detection New Product Perception Brand Perception Reputation Management

Key Players – Radian6, SAS and IBM, SentsCheck

Why use Sentiment Analysis?

THANK YOU

• ‘Allan Ziskey’. (2013). Pebble Watch User Reviews. CNET. Available: http://www.cnet.com/uk/products/pebble-watch/user-reviews/

• Burkeman, O. (2012). The Happiest Day of the Year and China Bottoming. Available: http://blog.marketpsych.com/2012/12/the-happiest-day-of-year-and-china.html. Last accessed 7th may 2014.

• ClipDealer (2009) Video Footage Clip – Binary Internet Tunnel. ClipDealer. Available: http://us.clipdealer.com/video/media/231706

• Davidson, N. (2012). How to conduct video interviews successfully. [online] Video Production & Marketing Blog | Mywebpresenters.com. Available at: http://www.mywebpresenters.com/articles/2012/08/how-conduct-video-interviews-successfully/ [Accessed 8 May. 2014].

• Facebook (2013) Facebook. Available: www.facebook.com/facebook• Garlitos, K. (2013) Betting Forums Attract New and Veteran Gamblers Alike CalvinAyre. Available:

http://calvinayre.com/2013/09/24/sports/betting-forums-attract-new-and-veteran-gamblers-alike/• Grimes, S. (2011). The*Truth*About*Sentiment*&*Natural* Language*Processing. 1st ed. [ebook] unknown: unknown, p.2.

Available at: https://synthesio.com/corporate/wp-content/uploads/2010/11/SYNTHESIO-NLP.pdf [Accessed 7 May. 2014].• IBM. About IBM. Available: http://www.ibm.com/ibm/ie/en/• iCopyright, (2014). 4 Online Content Strategies for Financial Publishers - iCopyright. [online] Available at:

http://info.icopyright.com/syndication/4-online-content-strategies-for-financial-publishers [Accessed 8 May. 2014].• Jenkins, C.J. (2009). How Sentiment Analysis Works. Slideshare. Available: http://www.slideshare.net/mcjenkins/how-

sentiment-analysis-works• King, R. (2014). Trading on a World of Sentiment. 1st ed. [ebook] San Francisco: Bloomberg, pp.1,2,3. Available at:

http://battleofthequants.com/TradingonaWorldofSentiment_Bloomberg_MarketPsych.pdf [Accessed 8 May. 2014].• Liddy, E. D. (2001) “Natural language processing”• Liddy, E. D. (2001). Natural language processing.• Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of natural language processing, 2, 627-666.

Bibliography

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