Roses are Red, Violets are Blue:Detection of Valid Sentiment-Target Pairs

Svitlana Vakulenko Albert Weichselbraun Arno Scharl

MODUL University ViennaUniversity of Applied Sciences Chur

International Conference on Web IntelligenceOctober 13–16, 2016 in Omaha, Nebraska, USA

Motivation

Roses are red, violets are blue

T1 S1 T2 S2

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 2 / 15

Application: Sentiment Analysis

Fine-grained sentiment analysis in product reviews

Example

The design is outstanding, but the sound quality is poor

Brand monitoring on-line (social) media

Example

[Apple, iPad , iPhone,MacBook,TimCook, ...]The weather was disappointing during Tim Cook’s visit to India.

Stock prediction using Twitter sentiment analysis

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 3 / 15

Problem Statement

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 4 / 15

Related Work

Sequence Labeling (Joint Model)[Yang and Cardie, 2013, Deng and Wiebe, 2015]

Classification (Sentiment/Target/Relation)[Kessler et al., 2010]

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 5 / 15

Feature Engineering

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 6 / 15

Feature Groups

Sentiment/Target (ST Penn: [VBP,NN])

Lexical Path (L Penn: [TO,VB,DT]; L Dist: 3)

Dependency Path (D Penn: [TO,VB]; D Rels: [OPRD,IM,OBJ])

I like to drive the car

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 7 / 15

Datasets

JDPA Sentiment Corpus1: customer reviews

MPQA Opinion Corpus 2.02: news articles

1https://verbs.colorado.edu/jdpacorpus/2http://mpqa.cs.pitt.edu/corpora/mpqa_corpus/mpqa_corpus_2_0/

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 8 / 15

Logistic Regression

Binary classification: P(Y = 1|z) =1

1 + e−z

z = wx + b

Regression coefficients:

logP(Y = 1|z)

1− P(Y = 1|z)= log

P(Y = 1|z)

P(Y = 0|z)= z = wx + b

w →log odds ratio

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 9 / 15

Patterns

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 10 / 15

Evaluation Results

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 11 / 15

Contribution: Selected 8

No Sentiment/Target featuresOther sentiments/targets on the lexical/dependency pathPOS-tag groups on the dependency/lexical pathDependency relations

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 12 / 15

Conclusions & Future Work

1 Feature evaluation is important!

2 Feature engineering is hard:TODO: Unsupervised feature learning (black box)

3 Proximity-based baseline is strong!

4 TODO: Evaluation on real-world datasets(w/o gold-standard annotations)

5 TODO: Utilize patterns to detect sentiments/targets (evaluation)

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 13 / 15

Contact

Slides: http://www.slideshare.net/svakulenko/

E-mail: svitlana.vakulenko@modul.ac.atMODUL University ViennaDepartment of New Media TechnologyAm Kahlenberg 1, 1190 Vienna, Austriahttp://vendi12.github.io/

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 14 / 15

Bibliography I

Deng, L. and Wiebe, J. (2015).Joint prediction for entity/event-level sentiment analysis usingprobabilistic soft logic models.In Proceedings of the Conference on Empirical Methods in NaturalLanguage Processing (EMNLP 15).

Kessler, J. S., Eckert, M., Clark, L., and Nicolov, N. (2010).The ICWSM 2010 JDPA sentiment corpus for the automotivedomain.In Proceedings of the 4th International AAAI Conference on Weblogsand Social Media Data Workshop Challenge (ICWSM-DWC 2010).

Yang, B. and Cardie, C. (2013).Joint inference for fine-grained opinion extraction.In Proceedings of the 51st Annual Meeting of the Association forComputational Linguistics (ACL 13), pages 1640–1649.

Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 15 / 15