The document discusses sentiment classification, which involves classifying documents based on the overall sentiment expressed. It outlines different approaches to sentiment classification, including lexical approaches that use sentiment dictionaries, supervised machine learning approaches that learn from annotated data, and semi-supervised and cross-domain approaches that can be applied when labeled data is limited. Commercial applications of sentiment classification are discussed, such as understanding customer feedback and predicting trends.

1. Semi­supervised vs. Cross­domain Graph­based Learning for Sentiment
Classification
Natalia Ponomareva
Statistical Cybermetrics Research Group, University of Wolverhampton, UK
December 21, 2013
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2. What is sentiment classification?
Task within the research field of Sentiment Analysis.
It concerns classification of documents on the basis of overall sentiments expressed by their
authors.
Different scales can be used:
positive/negative; positive, negative and neutral; rating: 1*, 2*, 3*, 4*, 5*;
Example “The film was fun and I enjoyed it.” ⇒ positive “The film lasted too long and I got bored.”
⇒ negative
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3. Outline
1
Background
Introduction Main approaches Motivation
2
Graph­based algorithms Label propagation Modifications to LP Graph construction
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4. Outline
3
Data and their characteristics
Preprocessing Baseline results Data characteristics
4 Semi­supervised vs. cross­domain graph­based learning
Semi­supervised experiments Cross­domain experiments Discussion
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5. Outline
1
Background
Introduction Main approaches Motivation
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6. Why do we need sentiment classification
“What other people think” has always been important for people in their decision­making
process.
Which cell phone to buy? Is it worth watching this movie? Before the development of the Internet
technologies, people used to ask friends. Now the Internet contains a huge amount of opinions in
forums, blogs, social networks, review sites, etc. However, it is not easy to find and analyse
opinions.
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7. Commercial applications
Understanding customer feedback. What is customer opinions about some specific company
products? Brand analysis, reputation management. What is customer opinions about the
company? Market survey. What do customers think about the products of the competitors?
Trend prediction. Will be people satisfied with the products in the future?
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8. Approaches to Sentiment Classification
Lexical approaches;
Supervised;
Semi­supervised and unsupervised;
Cross­domain.
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9. Lexical approaches
Use of dictionaries of sentiment words with a given semantic orientation. Dictionaries are built
either manually or (semi­)automatically. A special scoring function is applied in order to calculate
the final semantic orientation of a text.
Example lightweight +3, good +4, ridiculous ­2 Lightweight, stores a ridiculous amount of books
and good battery life. SO SO
1
2
= = max{|3|,|4|,|−2|} 3+4­2
3
= 12 3
∙ sign(max{|3|,|4|,|−2|})=4
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10. Supervised machine learning approaches
Learn sentiment phenomena from an annotated corpus.
Different Machine Learning methods were tested (NB, SVM, ME).
Usually demonstrated relatively high performance even with surface features, e.g. 82.9% on
movies reviews [Pang et al., 2002].
For review data ML approach performs better than lexical one when training and test data belong
to the same domain. For example, SO­CAL achieves only 76.4% on the same dataset [Taboada
et al., 2011].
But it needs substantial amount of annotated data.
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