A Survey of Sentiment Analysis

A Survey of Sentiment AnalysisBlockseminar “Intelligente Softwaresysteme” 2013/14 TU Berlin7 Feb 2014 Moritz Platt

Agenda

Introduction

▼

Algorithms

▼

Benchmarks

▼

Outlook

Intelligente Softwaresysteme 2013/14 2

Sentiment Analysis is an NLP Task

• Sentiment Analysis = Opinion Mining = Subjectivity Analysis

•Extract opinions on objects from text

•Working on natural language corpora•Research problem with a lot of applications•Relatively new research area, rapidly developing field•Related fields:

•Natural Language Processing• Social Media Analysis• Text Mining•Data Mining

Introduction > Algorithms > Benchmarks > Outlook


Accessing Opinions–Now and Then

Dot-Com Era and Beyond•Huge stream of opinionated text

•1.2 million daily blog posts [Zabin2008]

•45 million daily “status up-dates” on Facebook [Thomas2010]

•Often featuring opinions towards products or persons

Pre Dot-Com Era• Extensive measures

• Surveys•Opinion polls• Focus groups



Where are today’sopionated texts coming from?

Social Networks BlogsReviews



The Relationship Between Opinion Holders and Objects

• Edges between opinion holders and features represent opinions• The time aspect is usually ommited

John

f The voice quality of a particularmodel of a cellular phone

Jack

James

Opinion Holders

“Voice quality is wonderful.”

“Voice sounds terrible.”

“Speech quality is average.”

Features

o A particular modelof a cellular phone

ObjectsOpinionated Text Sentiment Value

f

f

f

f

PositiveNegative

Neutral

•Consider a set of product reviews for a particular model of a cellular phone



The Aspects of Opinions

StructureofanopinionasdefinedbyLiu[Liu2010]:

(oj, fjk, soijkl, hi, tl)•Object oj

The target of an opinion (e.g. product, person, event, organisation, topic)• Feature fjk

Components/Attributes of an object (e.g. battery life, camera resolution)• Sentiment Value soijkl

The orientantion of an opinion from a set of possible choices (e.g. positive, negative, neutral)

•Opinion Holder hi The person expressing the opinion

• Time tl The time at which the opinion is expressed



Algorithms


Approaching Sentiments Algorithmically

Unsupervised Methods

•No training data•Cross-domain applications

Supervised Methods

•Manually labelled training data•Usually superior to unsupervised

approaches

•Point-Wise Mutual Information

•Naïve Bayes Classification•Maximum Entropy Classification• Suppor Vector Machines



PMI-IR

•PMI: Point-wise mutual information• IR: Information retrieval

• Introduced 2002 as an unsupervised learning algorithm for classifying re-views [Turney2002]

•Based on the concept of PMI [Church1990]

•Measures the probability of the co-occurrence of words

PMI (word 1 ,w ord 2 )= log2p(word 1&word 2 )p(word 1 )p(word 2 )



PMI-IR

• Turney used the words poor and excellent as seeds for the algorithm

• SO is the sentiment orientation value•Positive SO-value for phrases more associated with excellent•Negative SO-value for phrases more associated with poor

• Improvement of results through IR component• Turney used AltaVista• uses the NEAR operator• h(query) is the number of hits returned given the query

SO(phrase )= PMI(phrase, “ excellent ”) PMI(phrase, “ poor”)

SO(phrase )= log2h(phrase NEAR“ excellent ”)h(“ poor”)h(phrase NEAR“ poor”)h(“ excellent ”)



NaïveBayesClassification

•Based on Bayes rule [Bayes1763]

• Simply trained, probalistic, effective• “Bag of words” of an input document d• Fixed set of classes C, e.g. C = {positive, negative}

• d can be reduced by omitting irrelevant words

All Words [Jurafsky2013]

I love this movie! It's sweet, but with satirical humor. The dialogue is great and the adventure scenes are fun… It manages to be whimsical and romantic while laughing at the conventions of the fairy tale genre. I would recommend it to just about anyone. I've seen it several times, and I'm always happy to see it again whenever I have a friend who hasn't seen it yet.!

Opinionated Words [Jurafsky2013]

x love xxxxxxxxxxxxxxxx sweet xxxxxxx satirical xxxxxxxxxx xxxxxxxxxxx great xxxxxxx xxxxxxxxxxxxxxxxxxx fun xxxx xxxxxxxxxxxxx whimsical xxxx romantic xxxx laughing xxxxxxxxx xxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxx recommend xxxxx xxxx xxxxxxxxxxxxxxxxxxxxxxxx several xxxxxxxxxxxxxxxxx xxxxx happy xxxxxxxxx again xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx



Naïve Bayes at work

1. Estimate P(c) of each class c by dividing the number of words in documents in c by the total number of words in the corpus2. Estimate the P(w|c) for all words w and classes c 3. The score for a document d to be in class c is

4. The most likely class for a document is the one with the highest score[Potts2011]



MaximumEntropyClassification

Ignoranceispreferabletoerror,andheislessremotefromthetruthwhobe-lievesnothingthanhewhobelieveswhatiswrong. — Thomas Jefferson

• Find weights for the features that maximize the likelihood of the training data

•Add constraints based on training data•More constraints = less entropy = distribution is closer to data

•More difficult to implement than Naïve Bayes[Potts2011]



Support Vector Machines

•Most intuitive for two-class, separable training data sets

• Find a vector to seperate data sets maximizing the margin (A vs B)

• The margin is limited by sup-port vectors

•Applicable to more complicated problems too• n-class space• inseperable training data

through transformation in higher dimensions

y

x

A

B



Benchmarks


Benchmarking Sentiment Analysis

•Benchmarking NB and ME with in-domain testing

[Potts2011]

•Binary classification•6.000 restaurant re-

views



Benchmarking Sentiment Analysis

•Benchmarking NB and ME with testing on a different domain

[Potts2011]


•Binary classification• Trained on 6.000 res-

taurant reviews• Tested on 6.000 prod-

uct reviews


Outlook


Opinionated Data in the Wild

•Works well under laboratory conditions•Proper spelling•Highly opinionated•Pre-defined object

• Still common NLP problems remain•Named entity recognition•Context specific meaning• Language Ambiguity

•Benchmarking corpora do not reflect real-world data quality



Opinionated Data in the Wild

• Social media data•Highly relevant•Huge corpus•Constantly growing

• Very noisy•Questionable text quality

• Spelling•Grammar

• Spam•Unclear context• Figurative speech• Slang• Irony

Warren Scott M.your mxf format is a joke. DO NOT BUY CANON

Like

•

Comment 21 January at 18:31

Leon H.Why battery 6L in my Canon sx280 have pretty low life

Like

•


Phil D.Youse guys did a solid on my wife's TI3- warranty expired lastmonth, but did the job good! Thanks CanonLike

•


Cole J.Got a canon gl1I love it, but a little fuzzyLike

•

Comment 28 January 2010

Authentic status updates from https://www.facebook.com/pages/Canon-Cameras



Conclusions / Future Work

•Development of algorithms is on the right track• Evolvement beyond binary classification•Algorithms will become more robust on less homogenous sources

• Industry aims to apply algorithms to noisy data



Appendix


References

article(Bayes1763)Bayes, T.An essay towards solving a problem in the doctrine of chancesPhil. Trans. of the Royal Soc. of London, 1763, Vol. 53, pp. 370-418

article(Church1990)Church, K.W. & Hanks, P.Word Association Norms, Mutual Information, and LexicographyComput. Linguist., MIT Press, 1990, Vol. 16(1), pp. 22-29

misc(Jurafsky2013)Dan Jurafsky, E.NaïveBayesandTextClassification2013

inproceedings(Liu2010)Liu, B.Sentiment analysis and subjectivityHandbook of Natural Language Processing, Second Edition. Taylor and Francis Group, Boca2010

misc(Potts2011)Potts, C.SentimentSymposiumTutorial:Classifiershttp://sentiment.christopherpotts.net/classifiers.html2011


book(Thomas2010)Thomas, A. & Applegate, J.PayAttention!:HowtoListen,Respond,andProfitfromCustomerFeedbackWiley, 2010

inproceedings(Turney2002)Turney, P.D.Thumbsuporthumbsdown?SemanticorientationappliedtounsupervisedclassificationofreviewsProceedings 40th Annual Meeting of the ACL (2002)2002, pp. 417-424

misc(Zabin2008)Zabin, J. & Jefferies, A.Social Media Monitoring and Analysis: Generating Consumer Insights from Online ConversationAberdeen Group Benchmark Report, 2008


Picture Credit

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A Survey of Sentiment Analysis

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