Türkçe Hedef Tabanlı Duygu Analizi İçin Alt Görevlerin İncelenmesi – Hedef Terim, Hedef Kategori Ve Duygu Sınıfı Belirleme

Yıl 2018, Cilt: 11 Sayı: 1, 43 - 56, 31.01.2018

Fatih Samet Çetin Gülşen Eryiğit

https://doi.org/10.17671/gazibtd.325865

Cited By: 7

Öz

Geleneksel
olarak doküman veya cümle seviyesinde yürütülen duygu analizi çalışmaları,
hedef tabanlı duygu analizi çalışmalarının ortaya çıkması ile yeni bir seviyeye
taşınmıştır. Hedef tabanlı duygu analizi (Aspect
based sentiment analysis) kısaca, bir metnin içinde yer alan farklı
duyguların ilgili oldukları hedef varlıklar ile birlikte tespit edilmesi olarak
tanımlanabilir. Güncel tanımlamalar, hedef tabanlı duygu analizini, üç temel
alanla (hedef terim, hedef kategori ve duygu sınıfı) temsil edilen duygu
tanımlama gruplarını belirlemeyi amaçlayan aşamalı bir görev olarak
betimlemektedir. Bu makalede,  Türkçe
hedef tabanlı duygu analizi konusunda yapılan incelemeler sunulmaktadır. Yürütülen
çalışmalar, ABSA 2016 yarışmasındaki görevler (1- Hedef kategori belirleme, 2-
Hedef terim belirleme, 3- Hedef kategori ve hedef terimin aynı anda
belirlenmesi ve 4- Duygu sınıfı belirleme) takip edilerek tasarlanmış ve yine
burada sağlanan Türkçe restoran yorumları veri kümesi üzerinde değerlendirilmişlerdir.
Hedef kategori, hedef terim ve ikisinin aynı anda belirlenmesi görevleri için,
kelime vektörleri (word vectors) ve doğal dil işleme çıktıları (sözcük ve cümle
analizi bilgileri) kullanan koşullu rastgele alanlara (CRF – conditional random
fields) dayalı bir dizilim etiketleme algoritması tasarlanmış ve her üç görevi
de tek aşamada çözebildiği gösterilmiştir. 
Elde edilen sonuçlar ile bu ilk üç görev için literatürdeki en yüksek
başarımların elde edildiği görülmüştür: Hedef kategori belirlemede %66,7
F1-skoru, hedef terim belirleme %53,2 F1-skoru, hedef kategori ve hedef terimin
aynı anda belirlenmesinde %46,7 F1-skoru. Bunun yanı sıra, duygu sınıfı
belirleme için cümle analizi sonucunda hedef terime yakın kelimelerden özellik
seçimine dayalı bir lineer sınıflandırma yöntemi sunulmuş ve literatürde sınırlı
sistemler tarafından raporlanan en başarılı sonuç (%76,1 F1-skoru) elde
edilmiştir.

Anahtar Kelimeler

hedef tabanlı duygu analizi, Türkçe, doğal dil işleme

Investigation of Aspect Based Turkish Sentiment Analysis Subtasks – Identification of Aspect Term, Aspect Category And Sentiment Polarity

Öz

Sentiment analysis studies conducted traditionally
at document or sentence level have been moved to a new level with the emergence
of aspect based sentiment analysis studies. Aspect-based
sentiment analysis can be briefly defined as the detection of different opinions
contained within a text together with the target entities to which they relate.
Current definitions describe aspect based sentiment analysis as a gradual task
aiming to identify opinion tuples represented by three main fields (target
term, target category, sentiment class). This article presents our
investigations on aspect based Turkish sentiment analysis. The work carried out
in this article is designed by following ABSA 2016 competition tasks (1- Aspect
category identification, 2- Aspect term identification, 3- Identification of aspect
category and aspect term together and 4- sentiment category classification) and
evaluated on the Turkish restaurant reviews dataset provided in the same event.
For the first three tasks, a sequence labeling algorithm (based on conditional
random fields (CRF)) which uses word vectors and natural language processing
outputs (word and sentence analyses) is proposed and shown to solve these three
tasks in one step. Experimental results show that the proposed system achieves
the highest performances for these tasks: 66.7% F1-score for aspect category identification,
53.2% F1-score for aspect term identification, 46.7% F1-score for both aspect
category and aspect term at the same time. Additionally, a linear
classification method based on feature selection from positionally and
syntactically neighboring tokens is proposed for sentiment category
classification task and shown to perform as the best constrained system
reported in the literature with 76.1% F1-score.    

Anahtar Kelimeler

aspect based sentiment analysis, Turkish language, natural language processing

Kaynakça

• [1] Pang, B. and L. Lee, Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2008. 2(1–2): p. 1-135.
• [2] Liu, B., Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 2012. 5(1): p. 1-167.
• [3] Pang, B. and L. Lee. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. in Proceedings of the 42nd Meeting of the Association for Computational Linguistics (ACL'04), Main Volume, Barcelona, Spain. 2004. Association for Computational Linguistics.
• [4] Wilson, T., J. Wiebe, and P. Hoffmann. Recognizing contextual polarity in phrase-level sentiment analysis. in Proceedings of the conference on human language technology and empirical methods in natural language processing. 2005. Vancouver, British Columbia, Canada: Association for Computational Linguistics.
• [5] Pontiki, M., D. Galanis, H. Papageorgiou, I. Androutsopoulos, S. Manandhar, A.-S. Mohammad, M. Al-Ayyoub, Y. Zhao, B. Qin, O. De Clercq, V. Hoste, M. Apidianaki, X. Tannier, N. Loukachevitch, E. Kotelnikov, N. Bel, S.M. Jimenez-Zafra, and G. Eryiğit, SemEval-2016 Task 5: Aspect Based Sentiment Analysis. Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval ’16, San Diego, California, June 16-17, 2016, 2016: p. 19-30.
• [6] Pontiki, M., D. Galanis, H. Papageorgiou, S. Manandhar, and I. Androutsopoulos, SemEval-2015 Task 12: Aspect Based Sentiment Analysis. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, 2015: p. 486-495.
• [7] Pontiki, M., D. Galanis, J. Pavlopoulos, H. Papageorgiou, I. Androutsopoulos, and S. Manandhar, Semeval-2014 task 4: Aspect based sentiment analysis. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Dublin, Ireland, 2014: p. 27-35.
• [8] Torunoglu, D. and G. Eryigit. A cascaded approach for social media text normalization of Turkish. in Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM)@ EACL. 2014. Gothenburg, Sweden: Association for Computational Linguistics.
• [9] Eryiğit, G. and D. Torunoğlu-Selamet, Social Media Text Normalization for Turkish. Natural Language Engineering, 2017(Accepted for publication).
• [10] Kaya, M., G. Fidan, and I.H. Toroslu. Sentiment analysis of turkish political news. in Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 01. 2012. Macau, China: IEEE Computer Society.
• [11] Yıldırım, E., F.S. Çetin, G. Eryiğit, and T. Temel, The impact of NLP on Turkish sentiment analysis. TÜRKİYE BİLİŞİM VAKFI BİLGİSAYAR BİLİMLERİ ve MÜHENDİSLİĞİ DERGİSİ, 2015. 7(1 (Basılı 8).
• [12] Dehkharghani, R., B. YANIKOGLU, Y. SAYGIN, and K. Oflazer, Sentiment analysis in Turkish at different granularity levels. Natural Language Engineering, 2016: p. 1-25.
• [13] Eryiğit, G., J. Nivre, and K. Oflazer, Dependency parsing of Turkish. Computational Linguistics, 2008. 34(3): p. 357-389.
• [14] Eryigit, G. ITU Turkish NLP Web Service. in EACL. 2014.
• [15] Buchholz, S. and E. Marsi. CoNLL-X shared task on multilingual dependency parsing. in Proceedings of the Tenth Conference on Computational Natural Language Learning. 2006. New York City, New York: Association for Computational Linguistics.
• [16] Lafferty, J., A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. in Proceedings of the eighteenth international conference on machine learning, ICML. 2001. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
• [17] Alghunaim, A., A Vector Space Approach for Aspect-Based Sentiment Analysis. 2015, Massachusetts Institute of Technology.
• [18] Toh, Z. and J. Su, NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features. Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval ’16, San Diego, California, June 16-17, 2016, 2016: p. 282-288.
• [19] Okazaki, N., CRFsuite: a fast implementation of conditional random fields (CRFs). 2007.
• [20] Şahin, G.G. Turkish Word Embeddings. 2016; Available from: http://isguderg.ml/embedding.html.
• [21] Çetin, F.S., E. Yıldırım, C. Özbey, and G. Eryiğit, TGB at SemEval-2016 Task 5: Multi-Lingual Constraint System for As-pect Based Sentiment Analysis. Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval ’16, San Diego, California, June 16-17, 2016, 2016: p. 337-341.
• [22] Mohammad, S.M. and P.D. Turney, Nrc emotion lexicon. 2013, NRC Technical Report.
• [23] Tamchyna, A. and K. Veselovská, UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification. Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval ’16, San Diego, California, June 16-17, 2016, 2016: p. 367-371.
• [24] Kumar, A., S. Kohail, A. Kumar, A. Ekbal, and C. Biemann, IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis. Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval ’16, San Diego, California, June 16-17, 2016, 2016: p. 1129-1135.
• [25] Ruder, S., P. Ghaffari, and J.G. Breslin, INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis. Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval ’16, San Diego, California, June 16-17, 2016, 2016.
• [26] Dehkharghani, R., Y. Saygin, B. Yanikoglu, and K. Oflazer, SentiTurkNet: a Turkish polarity lexicon for sentiment analysis. Language Resources and Evaluation, 2016. 50(3): p. 667-685.
• [27] Mohammad, S. NRC Word-Emotion Association Lexicon. 2013; Available from: http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm.
• [28] Şeker, G.A. and G. Eryiğit, Extending a CRF-based named entity recognition model for Turkish well formed text and user generated content. Semantic Web Journal, 2017(doi:10.3233/SW-170253).