Detailing Sentiment Analysis to Consider Entity Aspects: An Approach for Portuguese Short Texts

Keywords: sentiment analysis, machine learning, NLP

Abstract

Sentiment analysis is useful for identifying trends, or for discovering user preferences, which can later be applied to campaign targeting or recommendations. In this paper, we describe an approach to classify the sentiment polarity regarding aspects, and how this technique was used in a previous system, for short texts in Portuguese, giving it greater sensitivity to detail.

Aspect extraction is done by locating candidates for aspect as expressions having a relationship with the entity and possibly some polarized term, through rules based on POS tags. For each aspect, the sentiment polarity is determined by a Maximum Entropy classifier, whose features depend on the entity mention, on the aspect and its support text, including negation detection, bigrams, POS tags, and sentiment lexicon-based polarity clues. For aspect sentiment, our classifier evaluation indicated a precision of 68% for the positive class and 73% for the negative class, with the dataset used in our research.

References

(1) Catherine E. Tucker (2014) Social Networks, Personalized Advertising, and Privacy Controls. Journal of Marketing Research: October 2014, Vol. 51, No. 5, pp. 546-562.

(2) Pang, Bo & Lee, Lillian (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, Vol. 2(1-2), pp. 1-135.

(3) J. Saias, R. Silva, E. Oliveira, and R. Ruiz (2015). “Combining overall and target oriented sentiment analysis over portuguese text from social media,” Transactions on Machine Learning and Artificial Intelligence, vol. 3, pp. 46–55, June 2015.

(4) José Saias (2015). Sentiue: Target and Aspect based Sentiment Analysis in SemEval-2015 Task 12. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, USA. June 2015. p. 767-771, ACL

(5) E. Dovdon and J. Saias (2017). “ej-sa-2017 at semeval-2017 task 4: Experiments for target oriented sentiment analysis in twitter,” in Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), (Vancouver, Canada), pp. 635–638, Association for Computational Linguistics

(6) Silva, N. F. F. (2016). Análise de sentimentos em textos curtos provenientes de redes sociais. Tese de Doutorado, Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos. Brasil.

(7) Silva, L. (2018) Análise de Sentimentos em língua portuguesa do Brasil. Last accessed January 2018, at: https://www.kaggle.com/leandrodoze/sentiment-analysis-in-portuguese

(8) Balage Filho, P. P. (2017). Aspect extraction in sentiment analysis for portuguese language. Tese de Doutorado, Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, Brasil.

(9) Repustate. Sentiment analysis, social media sentiment and text analytics. Last accessed February 2018, at: https://www.repustate.com/portuguese-sentiment-analysis/

(10) S. Rosenthal, N. Farra and P. Nakov (2017). SemEval-2017 Task 4: Sentiment Analysis in Twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). Pages: 502–518. ACL, 2017.

(11) K. Cortis et al. (2017). SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). Pages: 519–535. ACL, 2017.

(12) Mengxiao Jiang, et al. (2017). Ecnu at semeval-2017 task 5: An ensemble of regression algorithms with effective features for fine-grained sentiment analysis in financial domain. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval2017). Association for Computational Linguistics, Vancouver, Canada.

(13) McCallum, Andrew Kachites (2002). "MALLET: A Machine Learning for Language Toolkit". http://mallet.cs.umass.edu

(14) M. J. Silva et al., Building a Sentiment Lexicon for Social Judgement Mining. In Lecture Notes in Computer Science (LNCS) / Lecture Notes in Artificial Intelligence (LNAI), International Conference on Computational Processing of Portuguese (PROPOR), Coimbra, 2012.

(15) Bing Liu (2010). Sentiment analysis and subjectivity. In Handbook of Natural Language Processing, 2nd Edition. Taylor and Francis Group, Boca, 2010.

(16) José Saias (2017). Ludificação: experiências para construção e marcação de um corpus para Análise de Sentimentos. In I Congresso Luso-Extremadurense de Ciências e Tecnologia, Universidade de Évora. Outubro de 2017. ISBN: 978-989-8550-45-3

(17) Kim, B. (2015). Understanding Gamification. Library Technology Reports, 51(2), 1–35

Published
2018-05-03