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.

• 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?)

• Thomas
• Wei Wei

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.

Decide the sentiment orientation of a given piece of opinion.

• 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)

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?

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?

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?

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.

Detect whether opinions that are written by spammers.

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

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 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?

• 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?

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

• 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/

• Movie Review Data

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

• Customer Review Data

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

• MPQA Opinion Corpus

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

• SentiWordNet

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

• NLTK - Natural Language Processing Toolkits for Python

http://nltk.sourceforge.net/

• WordNet

http://wordnet.princeton.edu/

• The Sentiment & Affect Yahoo! Group

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

• GI - General Inquirer

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

• LDC Catalog

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

• Opinmind

http://opinmind.com/

• Data Mining Resources

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

• 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/