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

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

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Presented at the International Conference on Web Intelligence October 15, 2016 in Omaha, Nebraska, USA.

Abstract—This paper investigates the linking of sentiments to their respective targets, a sub-task of fine-grained sentiment analysis. Many different features have been proposed for this task, but often without a formal evaluation. We employ a recursive feature elimination approach to identify features that optimize predictive performance. Our experimental evaluation draws upon two corpora of product reviews and news articles annotated with sentiments and their targets. We introduce competitive baselines, outline the performance of the proposed approach, and report the most useful features for sentiment target linking. The results help to better understand how sentiment-target relations are expressed in the syntactic struc- ture of natural language, and how this information can be used to build systems for fine-grained sentiment analysis.

Presented at the International Conference on Web Intelligence October 15, 2016 in Omaha, Nebraska, USA.

Abstract—This paper investigates the linking of sentiments to their respective targets, a sub-task of fine-grained sentiment analysis. Many different features have been proposed for this task, but often without a formal evaluation. We employ a recursive feature elimination approach to identify features that optimize predictive performance. Our experimental evaluation draws upon two corpora of product reviews and news articles annotated with sentiments and their targets. We introduce competitive baselines, outline the performance of the proposed approach, and report the most useful features for sentiment target linking. The results help to better understand how sentiment-target relations are expressed in the syntactic struc- ture of natural language, and how this information can be used to build systems for fine-grained sentiment analysis.

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

  1. 1. Roses are Red, Violets are Blue: Detection of Valid Sentiment-Target Pairs Svitlana Vakulenko Albert Weichselbraun Arno Scharl MODUL University Vienna University of Applied Sciences Chur International Conference on Web Intelligence October 13–16, 2016 in Omaha, Nebraska, USA
  2. 2. 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
  3. 3. 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
  4. 4. Problem Statement Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 4 / 15
  5. 5. 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
  6. 6. Feature Engineering Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 6 / 15
  7. 7. 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
  8. 8. 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
  9. 9. Logistic Regression Binary classification: P(Y = 1|z) = 1 1 + e−z z = wx + b Regression coefficients: log P(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
  10. 10. Patterns Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 10 / 15
  11. 11. Evaluation Results Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 11 / 15
  12. 12. Contribution: Selected 8 No Sentiment/Target features Other sentiments/targets on the lexical/dependency path POS-tag groups on the dependency/lexical path Dependency relations Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 12 / 15
  13. 13. 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
  14. 14. Contact Slides: http://www.slideshare.net/svakulenko/ E-mail: svitlana.vakulenko@modul.ac.at MODUL University Vienna Department of New Media Technology Am Kahlenberg 1, 1190 Vienna, Austria http://vendi12.github.io/ Vakulenko et al. (MODUL University Vienna) Detection of Valid Sentiment-Target Pairs WI’16 in Omaha, Nebraska, USA 14 / 15
  15. 15. Bibliography I Deng, L. and Wiebe, J. (2015). Joint prediction for entity/event-level sentiment analysis using probabilistic soft logic models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 15). Kessler, J. S., Eckert, M., Clark, L., and Nicolov, N. (2010). The ICWSM 2010 JDPA sentiment corpus for the automotive domain. In Proceedings of the 4th International AAAI Conference on Weblogs and 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 for Computational 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

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