Table of Contents

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

Supervisors:

Students:

What is opinion?

Subjective views on a certain topic

Views can be:

Topics:

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

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?

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”,

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

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.

Ontology

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

Scalability

Related Software Packages for Opinion Mining

Opinion Mining Related Resources

Research Papers

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

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

Datasets

http://www.cs.cornell.edu/people/pabo/movie%2Dreview%2Ddata/

http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html

http://www.cs.pitt.edu/mpqa/databaserelease/

Tools

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

http://nltk.sourceforge.net/

http://wordnet.princeton.edu/

Web Resources

http://groups.yahoo.com/group/SentimentAI

http://www.webuse.umd.edu:9090/ http://www.webuse.umd.edu:9090/tags/

http://www.ldc.upenn.edu/Catalog/

http://opinmind.com/

http://www.kdnuggets.com/index.html

Related Conferences

http://www.sigir.org

http://www.cikm.org

http://www.ideal2008.org/

http://www.sigkdd.org

http://www.aaai.org

http://www.iw3c2.org/

http://trec.nist.gov/

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

http://wsdm2009.org/

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

http://wi-consortium.org/

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

References