Mining Software Quality from Software Reviews: Research Trends and Open Issues
Issa Atoum, Ahmed Otoom "Mining Software Quality from Software Reviews: Research Trends and Open Issues". International Journal of Computer Trends and Technology (IJCTT) V31(2):74-83, January 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
Software review text fragments have
considerably valuable information about users?
experience. It includes a huge set of properties
including the software quality. Opinion mining or
sentiment analysis is concerned with analyzing textual
user judgments. The application of sentiment analysis
on software reviews can find a quantitative value that
represents software quality. Although many software
quality methods are proposed they are considered
difficult to customize and many of them are limited.
This article investigates the application of opinion
mining as an approach to extract software quality
properties. We found that the major issues of software
reviews mining using sentiment analysis are due to
software lifecycle and the diverse users and teams.
References
[1] A. Ghose and P. G. Ipeirotis, “Estimating the Helpfulness and
Economic Impact of Product Reviews: Mining Text and
Reviewer Characteristics,” IEEE Trans. Knowl. Data Eng.,
vol. 23, no. 10, pp. 1498–1512, 2011.
[2] B. Liu and L. Zhang, “A Survey of Opinion Mining and
Sentiment Analysis,” in Mining Text Data, C. Aggarwal,
Charu C. and Zhai, Ed. Springer US, 2012, pp. 415–463.
[3] B. Pang and L. Lee, “Opinion mining and sentiment
analysis,” Found. trends Inf. Retr., vol. 2, no. 1–2, pp. 1–135,
2008.
[4] B. Liu, “Sentiment Analysis and Opinion Mining,” Synth.
Lect. Hum. Lang. Technol., vol. 5, no. 1, pp. 1–167, May
2012.
[5] D. A. Garvin, “What does product quality really mean,” Sloan
Manage. Rev., vol. 26, no. 1, pp. 25–43, 1984.
[6] I. Atoum and C. H. Bong, “A Framework to Predict Software
„Quality in Use? from Software Reviews,” in Proceedings of
the First International Conference on Advanced Data and
Information Engineering (DaEng-2013), vol. 285, J.
Herawan, Tutut and Deris, Mustafa Mat and Abawajy, Ed.
Kuala Lumpur: Springer Singapore, 2014, pp. 429–436.
[7] W. Leopairote, A. Surarerks, and N. Prompoon, “Evaluating
software quality in use using user reviews mining,” in 10th
International Joint Conference on Computer Science and
Software Engineering (JCSSE), 2013, pp. 257–262.
[8] S. M. Basheer and S. Farook, “Movie Review Classification
and Feature based Summarization of Movie Reviews,” Int. J.
Comput. Trends Technol., vol. 4, 2013.
[9] Y. M. Mileva, V. Dallmeier, M. Burger, and A. Zeller,
“Mining trends of library usage,” Proc. Jt. Int. Annu. ERCIM
Work. Princ. Softw. Evol. Softw. Evol. Work. - IWPSE-Evol
?09, p. 57, 2009.
[10] B. Liu, “Sentiment analysis and subjectivity,” Handb. Nat.
Lang. Process., vol. 2, p. 568, 2010.
[11] ISO/IEC, “ISO/IEC 25010: 2011, Systems and software
engineering--Systems and software Quality Requirements and
Evaluation (SQuaRE)--System and software quality models,”
International Organization for Standardization, Geneva,
Switzerland, 2011.
[12] H. J. La and S. D. Kim, “A model of quality-in-use for
service-based mobile ecosystem,” in 2013 1st International
Workshop on the Engineering of Mobile-Enabled Systems
(MOBS), 2013, pp. 13–18.
[13] I. Atoum and C. H. Bong, “Measuring Software Quality in
Use: State-of-the-Art and Research Challenges,”
ASQ.Software Qual. Prof., vol. 17, no. 2, pp. 4–15, 2015.
[14] I. Atoum, C. H. Bong, and N. Kulathuramaiyer, “Building a
Pilot Software Quality-in-Use Benchmark Dataset,” in 9th
International Conference on IT in Asia, 2015.
[15] I. Atoum, C. H. Bong, and N. Kulathuramaiyer, “Towards
Resolving Software Quality-in-Use Measurement
Challenges,” J. Emerg. Trends Comput. Inf. Sci., vol. 5, no.
11, pp. 877–885, 2014.
[16] W. Leopairote, A. Surarerks, and N. Prompoon, “Software
quality in use characteristic mining from customer reviews,”
in 2012 Second International Conference on Digital
Information and Communication Technology and it?s
Applications (DICTAP), 2012, pp. 434–439.
[17] D. M. Blei, “Probabilistic topic models,” Commun. ACM, vol.
55, no. 4, pp. 77–84, Apr. 2012.
[18] I. Titov and R. McDonald, “Modeling online reviews with
multi-grain topic models,” in Proceedings of the 17th
international conference on World Wide Web, 2008, pp. 111–
120.
[19] M. Kantardzic, Data mining: concepts, models, methods, and
algorithms. Wiley-IEEE Press, 2011.
[20] X. Fei, G. Ping, and M. R. Lyu, “A Novel Method for Early
Software Quality Prediction Based on Support Vector
Machine,” 16th IEEE Int. Symp. Softw. Reliab. Eng., no.
Issre, pp. 213–222, 2005.
[21] A. Duric and F. Song, “Feature selection for sentiment
analysis based on content and syntax models,” Decis. Support
Syst., vol. 53, no. 4, pp. 704–711, Nov. 2012.
[22] K. Nigam, A. K. Mccallum, S. Thrun, and T. Mitchell, “Text
Classification from Labeled and Unlabeled Documents using
EM,” Mach. Learn., vol. 39, no. 2, pp. 103–134, 2000.
[23] M. Wen and Y. Wu, “Mining the Sentiment expectation of
nouns using Bootstrapping method,” in Proceedings of the 5th
international Joint conference on natural Language
Processing (iJcnLP-2010), 2011, pp. 1423–1427.
[24] Z. Zhai, B. Liu, J. Wang, H. Xu, and P. Jia, “Product Feature
Grouping for Opinion Mining,” Intell. Syst. IEEE, vol. 27, no.
4, pp. 37–44, 2012.
[25] S. T. W. Wendy, B. C. How, and I. Atoum, “Using Latent
Semantic Analysis to Identify Quality in Use ( QU )
Indicators from User Reviews,” in The International
Conference on Artificial Intelligence and Pattern Recognition
(AIPR2014), 2014, pp. 143–151.
[26] S. Deerwester and S. Dumais, “Indexing by latent semantic
analysis,” J. Am. Soc. Inf. Sci., vol. 41, no. 6, pp. 391–407,
Sep. 1990.
[27] T. K. Landauer, P. W. Foltz, and D. Laham, “An introduction
to latent semantic analysis,” Discourse Process., vol. 25, no.
2–3, pp. 259–284, 1998.
[28] T. Hofmann, “Probabilistic latent semantic indexing,” in
Proceedings of the 22nd annual international ACM SIGIR
conference on Research and development in information
retrieval, 1999, pp. 50–57.
[29] S.-M. Kim and E. Hovy, “Determining the sentiment of
opinions,” in Proceedings of the 20th international
conference on Computational Linguistics, 2004.
[30] N. Kobayashi, K. Inui, and Y. Matsumoto, “Extracting
aspect-evaluation and aspect-of relations in opinion mining,”
in Proceedings of the 2007 Joint Conference on Empirical
Methods in Natural Language Processing and Computational
Natural Language Learning (EMNLP-CoNLL), 2007, pp.
1065–1074.
[31] B. Pang and L. Lee, “A sentimental education: sentiment
analysis using subjectivity summarization based on minimum
cuts,” in Proceedings of the 42nd Annual Meeting on
Association for Computational Linguistics, 2004.
[32] J. M. Wiebe, R. F. Bruce, and T. P. O?Hara, “Development
and use of a gold-standard data set for subjectivity
classifications,” in Proceedings of the 37th annual meeting of
the Association for Computational Linguistics on
Computational Linguistics, 1999, pp. 246–253.
[33] S.-M. Kim and E. Hovy, “Crystal: Analyzing predictive
opinions on the web,” in Proceedings of the 2007 Joint
Conference on Empirical Methods in Natural Language
Processing and Computational Natural Language Learning
(EMNLP-CoNLL), 2007, pp. 1056–1064.
[34] F. Benamara, V. Popescu, B. Chardon, Y. Mathieu, and
Others, “Towards context-based subjectivity analysis,” in
Proceedings of 5th International Joint Conference on Natural
Language Processing, 2011, pp. 1180–1188.
[35] L. Barbosa and J. Feng, “Robust sentiment detection on
Twitter from biased and noisy data,” in Proceedings of the
23rd International Conference on Computational Linguistics:
Posters, 2010, pp. 36–44.
[36] J. M. Wiebe, “Learning subjective adjectives from corpora,”
in Proceedings of the National Conference on Artificial
Intelligence, 2000, pp. 735–741.
[37] V. Hatzivassiloglou and K. R. McKeown, “Predicting the
semantic orientation of adjectives,” in Proceedings of the 35th
Annual Meeting of the Association for Computational
Linguistics and Eighth Conference of the European Chapter
of the Association for Computational Linguistics, 1997, pp.
174–181.
[38] E. Riloff and J. Wiebe, “Learning extraction patterns for
subjective expressions,” in Proceedings of the 2003
conference on Empirical methods in natural language
processing, 2003, pp. 105–112.
[39] M. Hu and B. Liu, “Opinion extraction and summarization on
the web,” in Proceedings Of The National Conference On
Artificial Intelligence, 2006, vol. 21, no. 2, p. 1621.
[40] S. Blair-Goldensohn, K. Hannan, R. McDonald, T. Neylon,
G. A. Reis, and J. Reynar, “Building a sentiment summarizer
for local service reviews,” in WWW Workshop on NLP in the
Information Explosion Era, 2008.
[41] P. D. Turney and M. L. Littman, “Measuring praise and
criticism: Inference of semantic orientation from association,”
ACM Trans. Inf. Syst., vol. 21, no. 4, pp. 315–346, Oct. 2003.
[42] J. Kamps, M. Marx, R. J. Mokken, and M. de Rijke, “Using
wordnet to measure semantic orientation of adjectives,” in
Proceedings of the Fourth International Conference on
Language Resources and Evaluation (LREC 2004), 2004, vol.
IV, pp. 1115–1118.
[43] L. Velikovich, S. Blair-Goldensohn, K. Hannan, and R.
McDonald, “The viability of web-derived polarity lexicons,”
in Human Language Technologies: The 2010 Annual
Conference of the North American Chapter of the Association
for Computational Linguistics, 2010, pp. 777–785.
[44] H. Kanayama and T. Nasukawa, “Fully automatic lexicon
expansion for domain-oriented sentiment analysis,” in
Proceedings of the 2006 Conference on Empirical Methods in
Natural Language Processing, 2006, pp. 355–363.
[45] X. Ding, B. Liu, and P. S. Yu, “A holistic lexicon-based
approach to opinion mining,” in Proceedings of the
international conference on Web search and web data mining,
2008, pp. 231–240.
[46] L. Zhang and B. Liu, “Identifying noun product features that
imply opinions,” in Proceedings of the 49th Annual Meeting
of the Association for Computational Linguistics: Human
Language Technologies: short papers, 2011, vol. 2, pp. 575–
580.
[47] W. Du, S. Tan, X. Cheng, and X. Yun, “Adapting information
bottleneck method for automatic construction of domainoriented
sentiment lexicon,” in Proceedings of the third ACM
international conference on Web search and data mining,
2010, pp. 111–120.
[48] G. Qiu, B. Liu, J. Bu, and C. Chen, “Expanding domain
sentiment lexicon through double propagation,” in
Proceedings of the 21st international jont conference on
Artifical intelligence, 2009, pp. 1199–1204.
[49] G. Qiu, B. Liu, J. Bu, and C. Chen, “Opinion word expansion
and target extraction through double propagation,” Comput.
Linguist., vol. 37, no. 1, pp. 9–27, 2011.
[50] S. Feng, R. Bose, and Y. Choi, “Learning general connotation
of words using graph-based algorithms,” in Proceedings of
the Conference on Empirical Methods in Natural Language
Processing, 2011, pp. 1092–1103.
[51] P. University, “About WordNet,” Princeton University, 2010.
[Online]. Available: http://wordnet.princeton.edu.
[52] J. Liu and S. Seneff, “Review sentiment scoring via a parseand-
paraphrase paradigm,” in Proceedings of the 2009
Conference on Empirical Methods in Natural Language
Processing: Volume 1 - Volume 1, 2009, pp. 161–169.
[53] L. Qu, G. Ifrim, and G. Weikum, “The Bag-of-opinions
Method for Review Rating Prediction from Sparse Text
Patterns,” in Proceedings of the 23rd International
Conference on Computational Linguistics, 2010, pp. 913–
921.
[54] P. D. P. Turney, “Thumbs up or thumbs down?: semantic
orientation applied to unsupervised classification of reviews,”
in Proceedings of the 40th Annual Meeting on Association for
Computational Linguistics, 2002, no. July, pp. 417–424.
[55] M. Hu and B. Liu, “Mining and summarizing customer
reviews,” in Proceedings of the tenth ACM SIGKDD
international conference on Knowledge discovery and data
mining, 2004, pp. 168–177.
[56] K. P. P. Shein and T. T. S. Nyunt, “Sentiment Classification
Based on Ontology and SVM Classifier,” in Second
International Conference on Communication Software and
Networks, 2010, pp. 169–172.
[57] H. Yu and V. Hatzivassiloglou, “Towards answering opinion
questions: separating facts from opinions and identifying the
polarity of opinion sentences,” in Proceedings of the 2003
conference on Empirical methods in natural language
processing, 2003, pp. 129–136.
[58] M. Gamon and A. Aue, “Pulse: Mining Customer Opinions
from Free Text,” in Advances in Intelligent Data Analysis VI,
vol. 3646, A. Famili, A.Fazel and Kok, JoostN. and Peña,
JoséM. and Siebes, Arno and Feelders, Ed. Springer Berlin
Heidelberg, 2005, pp. 121–132.
[59] R. Narayanan, B. Liu, and A. Choudhary, “Sentiment analysis
of conditional sentences,” in Proceedings of the 2009
Conference on Empirical Methods in Natural Language
Processing: Volume 1 - Volume 1, 2009, pp. 180–189.
[60] L. Zhuang, F. Jing, and X.-Y. Zhu, “Movie review mining
and summarization,” in Proceedings of the 15th ACM
international conference on Information and knowledge
management, 2006, pp. 43–50.
[61] L. Jiang, M. Yu, M. Zhou, X. Liu, and T. Zhao, “Targetdependent
twitter sentiment classification,” in Proceedings of
the 49th Annual Meeting of the Association for
Computational Linguistics: Human Language Technologies,
2011, vol. 1, pp. 151–160.
[62] W. Wei and J. A. Gulla, “Sentiment learning on product
reviews via sentiment ontology tree,” in Proceedings of the
48th Annual Meeting of the Association for Computational
Linguistics, 2010, pp. 404–413.
[63] K. Moilanen and S. Pulman, “Sentiment composition,” in
Proceedings of the Recent Advances in Natural Language
Processing International Conference, 2007, pp. 378–382.
[64] L. Polanyi and A. Zaenen, “Contextual Valence Shifters,” in
Computing Attitude and Affect in Text: Theory and
Applications, J. Shanahan, JamesG. and Qu, Yan and Wiebe,
Ed. Springer Netherlands, 2006, pp. 1–10.
[65] N. Jindal and B. Liu, “Identifying comparative sentences in
text documents,” in Proceedings of the 29th annual
international ACM SIGIR conference on Research and
development in information retrieval, 2006, pp. 244–251.
[66] W. Jin and H. H. Ho, “A novel lexicalized HMM-based
learning framework for web opinion mining.,” in Proceedings
of the 26th Annual International Conference on Machine
Learning, 2009, pp. 465–472.
[67] A. Mukherjee and B. Liu, “aspect extraction through Semi-
Supervised modeling,” in Proceedings of 50th anunal meeting
of association for computational Linguistics (ACL-2012),
2012, no. July, pp. 339–348.
[68] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?:
sentiment classification using machine learning techniques,”
in Proceedings of the ACL-02 conference on Empirical
methods in natural language processing - Volume 10, 2002,
no. July, pp. 79–86.
[69] T. Wilson, J. Wiebe, and R. Hwa, “Just how mad are you?
Finding strong and weak opinion clauses,” in Proceedings of
the National Conference on Artificial Intelligence, 2004, pp.
761–769.
[70] M. Hu and B. Liu, “Opinion feature extraction using class
sequential rules,” in Proceedings of the Spring Symposia on
Computational Approaches to Analyzing Weblogs, 2006, no.
3.
[71] B. Liu, M. Hu, and J. Cheng, “Opinion observer: analyzing
and comparing opinions on the Web,” in Proceedings of the
14th international conference on World Wide Web, 2005, pp.
342–351.
[72] A.-M. Popescu, B. Nguyen, and O. Etzioni, “OPINE:
extracting product features and opinions from reviews,” in
Proceedings of HLT/EMNLP on Interactive Demonstrations,
2005, pp. 32–33.
[73] W. Zhang, H. Xu, and W. Wan, “Weakness Finder: Find
product weakness from Chinese reviews by using aspects
based sentiment analysis,” Expert Syst. Appl., vol. 39, no. 11,
pp. 10283–10291, Sep. 2012.
[74] J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack, “Sentiment
analyzer: extracting sentiments about a given topic using
natural language processing techniques,” in Third IEEE
International Conference on Data Mining ICDM 2003., 2003,
pp. 427–434.
[75] L. Shang, H. Wang, X. Dai, and M. Zhang, “Opinion Target
Extraction for Short Comments,” PRICAI 2012 Trends Artif.
Intell., vol. 7458, pp. 434–439, 2012.
[76] A. Bhattarai, N. Niraula, V. Rus, and K. Lin, “A Domain
Independent Framework to Extract and Aggregate Analogous
Features in Online Reviews,” in Computational Linguistics
and Intelligent Text Processing, vol. 7181, A. Gelbukh, Ed.
Springer Berlin Heidelberg, 2012, pp. 568–579.
[77] L. Zhang, B. Liu, S. H. S. H. Lim, and E. O?Brien-Strain,
“Extracting and ranking product features in opinion
documents,” in Proceedings of the 23rd International Conference on Computational Linguistics: Posters, 2010, no.
August, pp. 1462–1470.
[78] Z. Hai, K. Chang, and G. Cong, “One seed to find them all:
mining opinion features via association,” in Proceedings of
the 21st ACM international conference on Information and
knowledge management, 2012, pp. 255–264.
[79] B. Wang and H. Wang, “Bootstrapping both product features
and opinion words from chinese customer reviews with crossinducing,”
in Proceedings of The Third International Joint
Conference on Natural Language Processing (IJCNLP),
2008.
[80] L. Ku, Y. Liang, and H. Chen, “Opinion extraction,
summarization and tracking in news and blog corpora,” in
Proceedings of AAAI-2006 Spring Symposium on
Computational Approaches to Analyzing Weblogs, 2006, pp.
100–107.
[81] L. Tesnière and J. Fourquet, Eléments de syntaxe structurale,
vol. 1965. Klincksieck Paris, 1959.
[82] I. Titov and R. McDonald, “A joint model of text and aspect
ratings for sentiment summarization,” in Proceedings of the
46th Annual Meeting of the Association for Computational
Linguistics, 2008, pp. 308–316.
[83] D. M. Blei and J. D. McAuliffe, “Supervised topic models,”
in Advances in Neural Information Processing Systems 20
(NIPS 2007), 2007, pp. 121–128.
[84] L. Chen, L. Qi, and F. Wang, “Comparison of feature-level
learning methods for mining online consumer reviews,”
Expert Syst. Appl., vol. 39, no. 10, pp. 9588–9601, Aug. 2012.
[85] Q. Su, X. Xu, GuoHonglei, Z. Guo, X. Wu, Z. Xiaoxun, B.
Swen, and Z. Su, “Hidden sentiment association in chinese
web opinion mining,” in Proceedings of the 17th
international conference on World Wide Web, 2008, pp. 959–
968.
[86] L. Rabiner, “A tutorial on hidden Markov models and
selected applications in speech recognition,” Proc. IEEE, vol.
77, no. 2, pp. 257–286, 1989.
[87] N. Jakob and I. Gurevych, “Extracting Opinion Targets in a
Single- and Cross-domain Setting with Conditional Random
Fields,” in Proceedings of the 2010 Conference on Empirical
Methods in Natural Language Processing, 2010, pp. 1035–
1045.
[88] J. Lafferty, A. Mccallum, and F. C. N. Pereira, “Conditional
Random Fields : Probabilistic Models for Segmenting and
Labeling Sequence Data,” in Proceedings of ICML?01, 2001,
vol. 2001, no. Icml, pp. 282–289.
[89] J. Yu, Z.-J. Zha, M. Wang, K. Wang, and T.-S. Chua,
“Domain-assisted product aspect hierarchy generation:
towards hierarchical organization of unstructured consumer
reviews,” in Proceedings of the Conference on Empirical
Methods in Natural Language Processing, 2011, pp. 140–
150.
[90] H. Jin, M. Huang, and X. Zhu, “Sentiment Analysis with
Multi-source Product Reviews,” Intell. Comput. Technol., pp.
301–308, 2012.
[91] Q. Mei, X. Ling, M. Wondra, H. Su, and C. Zhai, “Topic
sentiment mixture: modeling facets and opinions in weblogs,”
in Proceedings of the 16th international conference on World
Wide Web, 2007, pp. 171–180.
[92] F. Su and K. Markert, “From words to senses: a case study of
subjectivity recognition,” in Proceedings of the 22nd
International Conference on Computational Linguistics -
Volume 1, 2008, pp. 825–832.
[93] J. Hai, Zhen and Chang, Kuiyu and Kim, “Implicit Feature
Identification via Co-occurrence Association Rule Mining,”
in Computational Linguistics and Intelligent Text Processing,
A. Gelbukh, Ed. Springer Berlin Heidelberg, 2011, pp. 393–
404.
[94] Z. Zhai, B. Liu, H. Xu, and P. Jia, “Constrained LDA for
Grouping Product Features in Opinion Mining,” in Advances
in Knowledge Discovery and Data Mining, vol. 6634, J.
Huang, L. Cao, and J. Srivastava, Eds. Springer Berlin
Heidelberg, 2011, pp. 448–459.
[95] Z. Zhai, B. Liu, H. Xu, and P. Jia, “Grouping product features
using semi-supervised learning with soft-constraints,” in
Proceedings of the 23rd International Conference on
Computational Linguistics, 2010, pp. 1272–1280.
[96] S. Mukherjee and P. Bhattacharyya, “Feature Specific
Sentiment Analysis for Product Reviews,” in Computational
Linguistics and Intelligent Text Processing, vol. 7181, A.
Gelbukh, Ed. Springer Berlin / Heidelberg, 2012, pp. 475–
487.
[97] D. Andrzejewski, X. Zhu, and M. Craven, “Incorporating
domain knowledge into topic modeling via Dirichlet Forest
priors,” in Proceedings of the 26th Annual International
Conference on Machine Learning, 2009, vol. 382, no. 26, pp.
25–32.
[98] H. Guo, H. Zhu, Z. Guo, X. Zhang, and Z. Su, “Product
feature categorization with multilevel latent semantic
association,” in Proceedings of the 18th ACM conference on
Information and knowledge management, 2009, pp. 1087–
1096.
[99] G. Carenini, R. T. Ng, and E. Zwart, “Extracting knowledge
from evaluative text,” in Proceedings of the 3rd international
conference on Knowledge capture, 2005, pp. 11–18.
[100] A. Dempster, N. Laird, and D. Rubin, “Maximum likelihood
from incomplete data via the EM algorithm,” J. R. Stat. Soc.
Ser. B, pp. 1–38, 1977.
[101] R. Ng and A. Pauls, “Multi-document summarization of
evaluative text,” in In Proceedings of the 11st Conference of
the European Chapter of the Association for Computational
Linguistics, 2006.
[102] S. Tata and B. Di Eugenio, “Generating fine-grained reviews
of songs from album reviews,” in Proceedings of the 48th
Annual Meeting of the Association for Computational
Linguistics, 2010, pp. 1376–1385.
[103] K. Lerman, S. Blair-Goldensohn, and R. McDonald,
“Sentiment summarization: evaluating and learning user
preferences,” in Proceedings of the 12th Conference of the
European Chapter of the Association for Computational
Linguistics, 2009, pp. 514–522.
[104] J. Liu, Y. Cao, C.-Y. Lin, Y. Huang, and M. Zhou, “Lowquality
product review detection in opinion summarization,”
in Proceedings of the Joint Conference on Empirical Methods
in Natural Language Processing and Computational Natural
Language Learning (EMNLP-CoNLL), 2007, pp. 334–342.
[105] Y. Lu, H. Duan, H. Wang, and C. Zhai, “Exploiting structured
ontology to organize scattered online opinions,” in
Proceedings of the 23rd International Conference on
Computational Linguistics, 2010, pp. 734–742.
[106] Y. Lu, C. Zhai, and N. Sundaresan, “Rated aspect
summarization of short comments,” in Proceedings of the
18th international conference on World wide web, 2009, pp.
131–140.
[107] J. Wiebe, T. Wilson, and C. Cardie, “Annotating Expressions
of Opinions and Emotions in Language,” Lang. Resour. Eval.,
vol. 39, no. 2–3, pp. 165–210, 2005.
[108] C. Toprak, N. Jakob, and I. Gurevych, “Sentence and
expression level annotation of opinions in user-generated
discourse,” in Proceedings of the 48th Annual Meeting of the
Association for Computational Linguistics, 2010, pp. 575–
584.
[109] X. Ding and B. Liu, “Resolving object and attribute
coreference in opinion mining,” in Proceedings of the 23rd
International Conference on Computational Linguistics,
2010, pp. 268–276.
[110] C. Akkaya, J. Wiebe, and R. Mihalcea, “Subjectivity word
sense disambiguation,” in Proceedings of the 2009
Conference on Empirical Methods in Natural Language
Processing: Volume 1, 2009, pp. 190–199.
Keywords
Software Quality-in-use, Clustering, Topic
Models, Opinion Mining Tasks.