Opinion Mining - An Brief Introduction

This page is a brief introduction to opinion mining. It contains some background information for doing researches in opinion mining.

Opinion Mining Group

What is opinion?

Subjective views on a certain topic

Views can be:

  • Pros / Cons
  • Suggestions for improvement
  • Comparisons

Topics:

  • Any objects (usually noun)
  • e.g.
  • Products: Car, Music Player, Camera, etc.
  • People: Barack Obama, Hillary Clinton, etc.
  • Organization: Bank, Government, School, etc.
  • Others…

What is opinion mining?

Informally: Extract the opinions given in a piece of text.

Or, more formally: A recent discipline that studies the extraction of opinions using Information Retrieval (IR), Artificial Intelligence (AI), Natural Language Processing (NLP) techniques.

What's the big deal with opinion mining?

Motivating Scenario

  • People who wants to buy a camera
    • Look for comments and reviews
  • People who just bought a camera
    • Comment on it
    • Write down the usage experience
  • Camera Manufacturer
    • Get feedback from customer
    • Improve their products
    • Adjust Marketing Strategies

Big business, right?

Web 2.0 nowadays provides a great medium for people to share what they want to share. This provides a great source of unstructured information (especially opinions) that may be usually (makes a lot of money?)

Major Issues

Opinion Extraction

Identify the segments of text that contain opinions.

e.g. Opinions are in boldface

I have just entered into dslr world with 400d, before I used slr cameras.

400d is extremly well made, precise and overall feeling is vey good.

Sentiment Classification / Subjectivity Analyzes

Decide the sentiment orientation of a given piece of opinion.

What is Sentiment Orientation?

  • Polarity
    • Positive (e.g. This camera is great!)
    • Negative (e.g. The battery life is too short.)
    • Neutral
  • Polarity Scale?
    • (Most Negative) -10 … -5 … 0 (Neutral) … 5 … 10 (Most Positive)

e.g. The picture quality is good. (A positive opinion) e.g. The battery life is short. (A negative opinion)

Feature-Opinion Association

A problem proposed by Kam Tong CHAN. The problem is related to natural language processing:

Given a text with target features and opinions extracted, decide which opinions comment on which features.

It is known to be a difficult problem in natural language processing. Let's take a look at the following example (Originated from http://en.wikipedia.org/wiki/Natural_language_processing)

Consider the phrase “pretty little girls' school”,

  • Does the school look little?
  • Do the girls look little?
  • Do the girls look pretty?
  • Does the school look pretty?

Reference:

[2009, inproceedings]
Kam Tong Chan and Irwin King, "Let's Tango -- Finding the Right Couple for Feature-Opinion Association in Sentiment Analysis," in Proc. PAKDD 2009: Advances in Knowledge Discovery and Data Mining, 13th Pacific-Asia Conference, Bangkok, Thailand, 2009.

Advanced Issues

Target Identification

Which one (or Who) is being commented?

e.g. He is a kind person.

Who is “he”?

e.g. The camera is great!

Which camera model are you talking about?

Source Identification

Given a review text, identify who made the comment.

Achieving this will allow us to build a Question-Answering System.

e.g. Who support Obama to be the next U.S. president?

Opinion Summarization and Visualization

Given a set of documents (crawled the web / all the reviews from a particular forum / survey results , etc.), summarize the opinion expressed with respect to the target object.

e.g. For Camera

  • Picture Quality (+ve: 290, -ve 73)
  • Ease of use (+ve: 57, -ve: 10)
  • etc.

Opinion Spam Detection

Detect whether opinions that are written by spammers.

Why there are opinion spams?

  1. Someone may write something to promote its own image / products
  2. Someone may write something to hurt their enemies

Others

Linguistic Tools for Opinion Mining

[Domain-Specific] Sentiment lexicon

A lexicon that contains the sentiment orientation of each term. It may be a domain specific one or a general one.

  • is there a way to generate it automatically from a large corpus?
Ontology

Ontology is a structural description of concepts. It defines the terminologies and hierarchical relationships of a domain.

  • Who ontologies can be incorporated in opinion mining? e.g.:
    • Opinion Summarization
    • Processing Comparative Statements
  • Is there a way to generate them automatically?
  • Which ontology elements are essential for opinion mining? In other words, what should the ontology for opinion mining looks like?

Scalability

  • Can an opinion summarization system works as efficient as a search engine so that all the opinions on the web are crawled and user are able to search for any opinions?

Related Software Packages for Opinion Mining

  • WordNet, SentiWordNet
  • Thesaurus
  • Python
    • NLTK (Natural Language Processing Toolkits)
    • Numpy, Scipy
    • Matplotlib
  • Text Processing Tools
    • Sentence Splitters
    • POS (Part-of-speech) Taggers
    • Stemmers
  • Crawler

Opinion Mining Related Resources

Research Papers

  • Sentiment Classification bibliography

http://liinwww.ira.uka.de/bibliography/Misc/Sentiment.html

  • ACL Anthology - A Digital Archive of Research Papers in Computational Linguistics

http://acl.ldc.upenn.edu/

Datasets

Tools

  • SentiWordNet

http://sentiwordnet.isti.cnr.it/

  • NLTK - Natural Language Processing Toolkits for Python

http://nltk.sourceforge.net/

  • WordNet

http://wordnet.princeton.edu/

Web Resources

Related Conferences

  • SIGIR - ACM SIGIR Special Interest Group on Information Retrieval

http://www.sigir.org

  • CIKM - Conference on Information and Knowledge Management

http://www.cikm.org

  • IDEAL - International Conference on Intelligent Data Engineering and Automated Learning

http://www.ideal2008.org/

  • SIGKDD - ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

http://www.sigkdd.org

  • AAAI - Association for the Advancement of Artificial Intelligence

http://www.aaai.org

  • WWW - International World Wide Web Conferences

http://www.iw3c2.org/

  • TREC - Text REtrieval Conference

http://trec.nist.gov/

  • ACL-IJCNLP - A Joint Conference of the Annual Meeting of the Association for Computational Linguistics and International Joint Conference on Natural Language Processing

http://www.acl-ijcnlp-2009.org/

  • WSDM - ACM International Conference on Web Search and Data Mining

http://wsdm2009.org/

  • SIGDAT / EMNLP - Conference on Empirical Methods in Natural Language Processing

http://www.cs.jhu.edu/~yarowsky/sigdat.html

  • WI - ACM International Conference on Web Intelligence

http://wi-consortium.org/

  • SIGWEB

http://www.sigweb.org/about/

References

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