Finding Subjectivity Clues

Finding subjectivity clues; sentence and clause-level classification Reactions

Sentiment Retrieval Using Generative Models authors: Eguchi, K. & Lavrenko, V. read by: John Knox

Sentiment retrieval: combined IR and

sentiment classification Novel idea Does it work? No reported recall, low precision…but competitive with similar systems

Automatic Identification Of Sentiment Vocabulary read by: Michael Lipschultz authors: Wilson, T., Wiebe, J., Hoffmann, P.

Two step: neutral-polar then determine

polarity Discussion of the role “not” and “will” Reduce some errors by allowing neutral terms in stage 2 Relation to “Identifying Subjective Adjectives through Web-based Mutual Information,” Baroni, M. & Vegnaduzzo, S. This work is concerned with finding opinions Wilson et. al is concerned with the next step

Using Emoticons to reduce Dependency in Machine Learning Techniques authors: Read J. read by: Yaw Gyamfi

Lack of rigor in analysis Rare work in that it looks at temporal

dependency…but is it persuasive? Title is misleading – emoticons are used more for reduction of annotation costs

Identifying Expressions of Opinion in Context authors: Breck, E., Choi, Y. & Cardie, C. read by: Matt McGettigan

Reviewer impressed with performance (close

to human annotators)…but… Concern with evaluation standards Subjective phrases as a natural extension from subjective adjectives

Feature Subsumption for Opinion Analysis authors: Riloff, E., Patwardhan, S. & Wiebe, J. read by: Mahesh

Considering dependencies among features

can add considerable performance Considering POS as subsuming unigrams

Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews authors: Dave K., Lawrance S. and Pennock D.

Supposedly comparing IR vs. Machine

learning techniques, but the IR approach skews heavily towards machine learning approach Comparison to emoticon paper: explicit rating (self tagging) vs. automatic identification Granularity: some features that help at e.g. sentence level are less useful at the document level

Extracting Appraisal Expressions authors: Bloom, K., Garg, N. & Argamon., S. read by: Danielle Mowery

Significantly different annotation schema

compared to MPQA Author evaluated system output post facto Can’t evaluate precision Ample opportunity for bias

Major themes IR for sentiment analysis Problems with evaluation standards Weak standards Lack of rigor in analysis Not enough data supplied (e.g. accuracy only)

Increasing sophistication of features Multi-stage approaches Dependencies among features Levels of tagging (phrase/sentence/document)