Term-based Features for Sentiment Classification Yusuf Ziya IÅÄ±k 5 ...

More documents

Recommendations

Info

Motivation●●●●Trying to understand the reasons ofcontradicting results in the literature for termbasedfeatures.Obtaining an accurate and simple baseline forfurther studies.Investigating the possible benefits ofdiscriminative term selection.Investigating the possible benefits of addingproximity information to term frequencies.
●Pang 2002:Literature Review● Best accuracy with unigrams using binary weighting● Bigrams do not help●Dave 2003:● Bigrams and trigrams without unigrams do better● Simulation of NEAR operator gave slight improvementto bigrams.●Paltoglou 2010:● Any scaled tf weighting schemes do as well as binaryweighting.● Smoothed Delta-idf adds great improvement.● Gerani 2010:● Use proximity information to find documentscontaining opinions about a query.
Page 4 and 5: SMART Notation
Page 6 and 7: Unigram Results●●●●Bnn bett
Page 8 and 9: Discriminative Term Selection●●
Page 10 and 11: NEAR operator●●●Instead of us
Page 12 and 13: Results
Page 14: References

Term-based Features for Sentiment Classification Yusuf Ziya IÅÄ±k 5 ...

Create successful ePaper yourself

Delete template?

Save as template?

Term-based Features for Sentiment Classification Yusuf Ziya IÅÄ±k 5 ...