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Review of Sentiment Analysis and Opinion Mining Algorithms

Yıl 2017, Cilt: 3 Sayı: 1, 75 - 111, 24.06.2017

Ümit Can , Bilal Alataş

Öz

Sentiment Analysis or Opinion Mining is an important
field in text mining.  Nowadays the
products which are produced by companies or persons are reached to consumers
mercurially and reviews about these products issued on web pages. As understood
easily these reviews are very significant for producers. In addition to that,
Sentiment Analysis can be used from financial field to medicine field.
Sentiment Analysis investigates a text that has a positive, negative or a
neutral meaning. In general, we can imagine Sentiment Analysis as the
computational treatment of opinions, sentiments, and subjectivity of text.



In this study, a research about Sentiment Analysis has
been performed and Sentiment Analysis classification techniques have been
explained with its all parts.  Many
articles related with Sentiment Analysis have been studied and briefly
explained. Then, one application about Sentiment Analysis has been shown for
understanding more about Sentiment Analysis. Consequently, a general assessment
of this issue has been done and the study has been finished with the result
section.

Anahtar Kelimeler

Opinion mining sentiment analysis sentiment analysis algorithms sentiment analysis applications

Kaynakça

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Duygu Analizi ve Fikir Madenciliği Algoritmalarının İncelenmesi

Yıl 2017, Cilt: 3 Sayı: 1, 75 - 111, 24.06.2017

Ümit Can , Bilal Alataş

Öz

Duygu Analizi veya Fikir Madenciliği, metin
madenciliğinin önemli bir alanı ve son yılların önemli araştırma konularından
biridir. Günümüzde şirketlerin veya kişilerin ürettikleri ürünler çok hızlı bir
şekilde tüketiciye ulaşmakta ve bu ürünlerle ilgili yapılan yorumlarda gelişen
teknoloji ile beraber internet dünyasına yansımaktadır. Bu yorumların ne anlama
geldiği üreticiler için çok önemlidir. Bunun dışında Duygu Analizi veya Fikir
Madenciliği finanstan tutun da tıp alanına kadar birçok alanda kullanılabilir.
Duygu Analizi; bir metni ele alarak bu metnin olumlu, olumsuz veya tarafsız bir
içeriğe sahip olup olmadığını inceler. Genel olarak fikirlerin, duyguların ve
metinlerin nesnelliğinin hesaplanma işlemi de denilebilir.



Bu çalışma da, Duygu Analizi hakkında araştırma
yapılmış olup Duygu Analizi sınıflandırma teknikleri incelenerek tüm alt
bileşenleri ile beraber anlatılmıştır. Duygu Analizi ile ilgili birçok güncel
makale incelenmiş ve kısaca anlatılmıştır. En son olarak genel bir
değerlendirme ve sonuç yazılarak çalışma bitirilmiştir.




Anahtar Kelimeler

Duygu analizi duygu analizi algoritmalari duygu analizi uygulamalari fikir madenciliği

Kaynakça

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  • Wang, Q., Jin, Y., Yang, T., Cheng, S., 2017. An emotion-based independent cascade model for sentiment spreading. Knowledge-Based Systems, 116;86–93.
  • Whitelaw, C., Garg, N., Argamon, S., 2005. Using appraisal groups for sentiment analysis. In: Proceedings of the ACM SIGIR Conference on Information and Knowledge Management (CIKM). p. 625–31.
  • Wille, R., 1982. Restructuring lattice theory: an approach based on hierarchies of concepts. In: I. Rival, Reidel, Dordrecht-Boston. p. 445–70.
  • Williams, L., Bannister, C., Arribas-Ayllon, M., Preece, A., Spasic, I., 2015. The role of idioms in sentiment analysis. Expert Systems with Applications 42;7375–7385.
  • Wilson, T., Wiebe, J., Hoffman, P., 2005 Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on human language technology and empirical methods in natural language processing. Association for Computational Linguistics, p. 347-354
  • Wu, F., Huang, Y., Yuan, Z., 2017. Domain-specific sentiment classification via fusing sentiment knowledge from multiple sources. Information Fusion 35;26–37.
  • Wu, Q., Songbo, T., 2011. A two-stage framework for crossalan sentiment classification. Expert Syst Appl. 38:14269–75.
  • Xianghua, F., Guo., L, Yanyan, G., Zhiqiang, W., 2013. Multiaspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowl-Based Syst. 37:186–95.
  • Xu, K., Liao, S. S., Li, J., Song, Y., 2011.Mining comparative opinions from customer reviews for competitive intelligence. Decis Support Syst. 50: 743–54.
  • Xu, T., Peng, Q., Cheng, Y., 2012. Identifying the semanticorientation of terms using S-HAL for sentiment analysis. Knowl-Based Syst. 35:279–89.
  • Yan-Yan, Z., Bing, Q., Ting, L., 2010. Integrating intra-and inter-document evidences for improving sentence sentiment classification. Acta Automatica Sinica, 36.10: 1417-1425.
  • Yelena, M., Padmini, S., 2011. Exploring Feature Definition and Selection for Sentiment Classifiers. In: ICWSM.
  • Yu, L.C., Wu, J.L., Chann, P.C., Chu, H.S., 2013. Using a contextual entropy model to expand emotionwords and their intensity for the sentiment classification of stockmarket news. Knowl-Based Syst. 41:89–97.
  • Yu, Y., Wang. X., 2015. World Cup 2014 in the Twitter World: A big data analysis of sentiments in U.S. sports fans’ tweets, Computers in Human Behavior. 48, 392-400.
  • Zhang, T., Johnson, D., 2003. A robust risk minimization based named entity recognition system. In: Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003-Volume 4. Association for Computational Linguistics, p. 204-207.
  • Zhang, W., Xu, H., Wan, W., 2012. Weakness finder: find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Syst Appl. 39:10283–91.
  • Zhao, Y., Niu, K., He, Z., Lin, J., Wang, X., 2013. Text sentiment analysis algorithm optimization and platform development in social network. In: Computational Intelligence and Design (ISCID), 2013 Sixth International Symposium on. IEEE, p. 410-413.
  • Zhou, L., Li, B., Gao, W., Wei, Z., Wong, K., 2011. Unsupervised discovery of discourse relations for eliminating intra-sentence polarity ambiguities. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, p. 162-171.
  • Zirn, C., Niepert, M., Stuckenschmidt, H., Strube, M., 2011. Fine-Grained Sentiment Analysis with Structural Features. In: IJCNLP. p. 336-344.
Toplam 158 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Makaleler
Yazarlar

Ümit Can

Bilal Alataş Bu kişi benim

Yayımlanma Tarihi 24 Haziran 2017
Gönderilme Tarihi 6 Nisan 2017
Kabul Tarihi 13 Haziran 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 3 Sayı: 1

Kaynak Göster

APA Can, Ü., & Alataş, B. (2017). Duygu Analizi ve Fikir Madenciliği Algoritmalarının İncelenmesi. International Journal of Pure and Applied Sciences, 3(1), 75-111.
AMA Can Ü, Alataş B. Duygu Analizi ve Fikir Madenciliği Algoritmalarının İncelenmesi. International Journal of Pure and Applied Sciences. Haziran 2017;3(1):75-111.
Chicago Can, Ümit, ve Bilal Alataş. “Duygu Analizi Ve Fikir Madenciliği Algoritmalarının İncelenmesi”. International Journal of Pure and Applied Sciences 3, sy. 1 (Haziran 2017): 75-111.
EndNote Can Ü, Alataş B (01 Haziran 2017) Duygu Analizi ve Fikir Madenciliği Algoritmalarının İncelenmesi. International Journal of Pure and Applied Sciences 3 1 75–111.
IEEE Ü. Can ve B. Alataş, “Duygu Analizi ve Fikir Madenciliği Algoritmalarının İncelenmesi”, International Journal of Pure and Applied Sciences, c. 3, sy. 1, ss. 75–111, 2017.
ISNAD Can, Ümit - Alataş, Bilal. “Duygu Analizi Ve Fikir Madenciliği Algoritmalarının İncelenmesi”. International Journal of Pure and Applied Sciences 3/1 (Haziran 2017), 75-111.
JAMA Can Ü, Alataş B. Duygu Analizi ve Fikir Madenciliği Algoritmalarının İncelenmesi. International Journal of Pure and Applied Sciences. 2017;3:75–111.
MLA Can, Ümit ve Bilal Alataş. “Duygu Analizi Ve Fikir Madenciliği Algoritmalarının İncelenmesi”. International Journal of Pure and Applied Sciences, c. 3, sy. 1, 2017, ss. 75-111.
Vancouver Can Ü, Alataş B. Duygu Analizi ve Fikir Madenciliği Algoritmalarının İncelenmesi. International Journal of Pure and Applied Sciences. 2017;3(1):75-111.
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