Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics 0470022744.pdf

Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics 0470022744.pdf

rapidshare social epidemiology

Rapidshare social epidemiology

Following the takedown of similar service Megaupload in 2012, RapidShare changed its business model to deter the use of its services for distribution of files to large numbers of anonymous users and to focus on personal subscription-only cloud-based file storage. Its popularity fell sharply as a result and, by the end of March 2015, RapidShare ceased to operate.

RapidShare was an online file hosting service that opened in 2002. In 2009, it was among the Internet's 20 most visited websites and claimed to have 10 petabytes of files uploaded by users with the ability to handle up to three million users simultaneously.

pattern design and grading

pattern design and grading

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A systematic literature review on opinion types and sentiment analysis techniques: Tasks and challenges

 

Abstract

Purpose: 

The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods. 

Design/methodology/approach: 

A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal. 

Findings: 

The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review. 

Research limitations/implications: 

The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions. 

Originality/value: 

Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.

• Al-Debei, M. M., Akroush, M. N., & Ashouri, M. I. (2015). Consumer attitudes towards online shopping: the effects of trust, perceived benefits, and perceived web quality. Internet Research, 25(5), 707-733.
• Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2016). Determinants of perceived usefulness of e-learning systems. Computers in Human Behavior, 64, 843-858.
• Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., & Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain. Journal of systems and software, 80(4), 571-583.
• Cambria, E. (2016). Affective computing and sentiment analysis. IEEE Intelligent Systems, 31(2), 102-107.
• Cambria, E., Poria, S., Bajpai, R., & Schuller, B. (2016) SenticNet 4: A semantic resource for sentiment analysis based on conceptual primitives. In: COLING, Osaka.
• Cambria, E., Wang, H., & White, B. (2014) Guest editorial: Big social data analysis. Knowledge-Based Systems 69, 1-2.
• Cambria, E., Grassi, M., Hussain, A., & Havasi, C. (2012) Sentic computing for social media marketing. Multimedia Tools and Applications 59(2), 557-577.
• Cambria, E., Benson, T., Eckl, C., & Hussain, A. (2012). Sentic PROMs: Application of sentic computing to the development of a novel unified framework for measuring health-care quality. Expert Systems with Applications, 39(2012), 10533–10543.
• Cambria, E., Fu, J., Bisio, F., & Poria, S. (2015). AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis. In: AAAI, 508-514.
• Cambria, E., Gastaldo, P., Bisio, F., & Zunino, R. (2015). An ELM-based model for affective analogical reasoning. Neurocomputing, 149, 443-455.
• Cambria, E., & Hussain, A. (2015). Sentic computing: a common-sense-based framework for concept-level sentiment analysis (Vol. 1): Springer.
• Cambria, E., Hussain, A., Durrani, T., Havasi, C., Eckl, C., & Munro, J. (2010). Sentic computing for patient centered applications. Paper presented at the IEEE 10th International Conference on Signal Processing Proceedings.
• Cambria, E., Hussain, A., Havasi, C., & Eckl, C. (2009). Common sense computing: from the society of mind to digital intuition and beyond Biometric ID Management and Multimodal Communication (pp. 252-259): Springer.
• Cambria, E., Mazzocco, T., & Hussain, A. (2013). Application of multi-dimensional scaling and artificial neural networks for biologically inspired opinion mining. Biologically Inspired Cognitive Architectures, 4(2013), 41-53.
• Cambria, E., Olsher, D., & Kwok, K. (2012). Sentic Activation: A Two-Level Affective Common Sense Reasoning Framework. Paper presented at the AAAI.
• Cambria, E., Song, Y., Wang, H., & Howard, N. (2014). Semantic multidimensional scaling for open-domain sentiment analysis. IEEE Intelligent Systems, 29(2), 44-51.
• Cambria, E., & White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational Intelligence Magazine, 9(2), 48-57.
• Chaturvedi, I., Cambria, E., Poria, S. & Bajpai, R. (2016) Bayesian Deep Convolutional Belief Networks for Subjectivity Detection. In: ICDM, Barcelona.
• Cohen, J. (1968). Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological bulletin, 70(4), 213.
• Cruz, F. L., Troyano, J. A., Enríquez, F., Ortega, F. J., & Vallejo, C. G. (2010). A knowledge-rich approach to feature-based opinion extraction from product reviews. Paper presented at the Proceedings of the 2nd international workshop on Search and mining user-generated contents.
• Dybå, T., & Dingsøyr, T. (2008). Empirical studies of agile software development: A systematic review. Information and software technology, 50(9), 833-859.
• Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of advanced nursing, 62(1), 107-115.
• Etzioni, O., Banko, M., Soderland, S., & Weld, D. S. (2008). Open information extraction from the web. Communications of the ACM, 51(12), 68-74.
• Faliagka, E., Tsakalidis, A., & Tzimas, G. (2012). An integrated e-recruitment system for automated personality mining and applicant ranking. Internet Research, 22(5), 551-568.
• Fleiss, J., Levin, B., & Paik, M. (2004). The measurement of interrater agreement (pp. 598–626). Statistical methods for rates and proportions, 3rd ed. John Wiley & Sons, Hoboken, NJ. Ganapathibhotla, M., & Liu, B. (2008). Mining opinions in comparative sentences. Paper presented at the Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1.
• Habernal, I., Ptáček, T., & Steinberger, J. (2014). Supervised sentiment analysis in Czech social media. Information Processing & Management, 50(5), 693-707.
• Havasi, C., Speer, R., & Holmgren, J. (2010). Automated color selection using semantic knowledge. Proceedings of AAAI CSK, Arlington, USA. Hou, F., & Li, G.-h. (2008). Mining chinese comparative sentences by semantic role labeling.
• Paper presented at the 2008 International Conference on Machine Learning and Cybernetics.
• Hou, F., & Li, G. H. (2008). Mining chinese comparative sentences by semantic role labeling. Paper presented at the Machine Learning and Cybernetics, 2008 International Conference on.
• Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative health research, 15(9), 1277-1288.
• Hu, M., & Liu, B. (2004). Mining opinion features in customer reviews. Paper presented at
• the Proceedings of the National Conference on Artificial Intelligence.
• Inayat, I., Salim, S. S., Marczak, S., Daneva, M., & Shamshirband, S. (2015). A systematic
• literature review on agile requirements engineering practices and challenges. Computers in Human Behavior, 51, 915-929.
• Jindal, N., & Liu, B. (2006a). Identifying comparative sentences in text documents. Paper presented at the Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval.
• Jindal, N., & Liu, B. (2006). Mining comparative sentences and relations. Paper presented at the AAAI.
• Jindal, N., & Liu, B. (2006b). Mining comparative sentences and relations. Paper presented at the Proceedings of the National Conference on Artificial Intelligence.
• Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. UK: Keele.
• Kitchenham, B., Pretorius, R., Budgen, D., Brereton, O. P., Turner, M., Niazi, M., & Linkman, S. (2010). Systematic literature reviews in software engineering–a tertiary study. Information and software technology, 52(8), 792-805.
• Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, 159-174.
• Lee, J., Park, D.-H., & Han, I. (2011). The different effects of online consumer reviews on consumers' purchase intentions depending on trust in online shopping malls: An advertising perspective. Internet Research, 21(2), 187-206
• Lee, M., Kim, M., & Peng, W. (2013). Consumer reviews: reviewer avatar facial expression and review valence. Internet Research, 23(2), 116-132.
• Li, S., Lin, C.-Y., Song, Y.-I., & Li, Z. (2013). Comparable entity mining from comparative questions. Knowledge and Data Engineering, IEEE Transactions on, 25(7), 1498-1509.
• Li, S., Lin, C. Y., Song, Y. I., & Li, Z. (2010). Comparable entity mining from comparative questions. Paper presented at the Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics.
• Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of natural language processing, 2, 627-666.
• Liu, B., Hu, M., & Cheng, J. (2005). Opinion observer: analyzing and comparing opinions on the Web. Paper presented at the Proceedings of the 14th international conference on World Wide Web.
• Loia, V., & Senatore, S. (2014). A fuzzy-oriented sentic analysis to capture the human emotion in Web-based content. Knowledge-Based Systems, 58, 75-85.
• Ma, Y., Cambria, E. & Gao, S. (2016) Label embedding for zero-shot fine-grained named entity typing. In: COLING, Osaka.
• Martínez-Cámara, E., Martín-Valdivia, M. T., & Ureña-López, L. A. (2011). Opinion classification techniques applied to a spanish corpus Natural Language Processing and Information Systems (pp. 169-176): Springer.
• Pacheco, C., & Garcia, I. (2012). A systematic literature review of stakeholder identification methods in requirements elicitation. Journal of systems and software, 85(9), 2171-2181.
• Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up?: sentiment classification using machine learning techniques. Paper presented at the Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10.
• Popescu, A.-M., Nguyen, B., & Etzioni, O. (2005). OPINE: Extracting product features and opinions from reviews. Paper presented at the Proceedings of HLT/EMNLP on interactive demonstrations.
• Poria, S., Gelbukh, A., Cambria, E., Hussain, A., & Huang, G.-B. (2014). EmoSenticSpace: A novel framework for affective common-sense reasoning. Knowledge-Based Systems, 69, 108-123.
• Poria, S., Cambria, E., Hussain, A., & Huang, G.-B. (2015) Towards an intelligent framework for multimodal affective data analysis. Neural Networks 63, 104-116.
• Poria, S., Cambria, E., Howard, N., Huang, G.-B., & Hussain, A. (2016) Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174, 50-59.
• Poria, S., Chaturvedi, I., Cambria, E. & Hussain, A. (2016) Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: ICDM, Barcelona.
• Poria, S., Cambria, E., Hazarika, D., & Vij, P. (2016) A deeper look into sarcastic tweets using deep convolutional neural networks. In: COLING, Osaka.
• Poria, S., Cambria, E., & Gelbukh, A. (2016) Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems 108, 42-49.
• Qazi, A., Fayaz, H., Wadi, A., Raj, R. G., Rahim, N., & Khan, W. A. (2015). The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review. Journal of cleaner production, 104, 1-12.
• Qazi, A., Raj, R. G., Tahir, M., Cambria, E., & Syed, K. B. S. (2014). Enhancing Business Intelligence by Means of Suggestive Reviews. The Scientific World Journal, 2014,11.
• Qazi, A., Raj, R. G., Tahir, M., & Naqvi, S. G. A. (2013). A Framework of Review Analysis for Enhancement of Business Decision Making. Paper presented at the 13th International Conference on Data Mining Workshops.
• Qazi, A., Raj, R. G., Tahir, M., Waheed, M., Khan, S. U. R., & Abraham, A. (2014). A Preliminary Investigation of User Perception and Behavioral Intention for Different Review Types: Customers and Designers Perspective. The Scientific World Journal, 2014, 8.
• Qazi, A., Syed, K. B. S., Raj, R. G., Cambria, E., Tahir, M., & Alghazzawi, D. (2016). A concept-level approach to the analysis of online review helpfulness. Computers in Human Behavior, 58, 75-81.
• Quigley, M. (2008). Encyclopedia of information ethics and security: IGI Global. Seuring, S., & Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of cleaner production, 16(15), 1699-1710.
• Smailović, J., Grčar, M., Lavrač, N., & Žnidaršič, M. (2014). Stream-based active learning for sentiment analysis in the financial domain. Information Sciences, 285, 181-203.
• Tamimi, N., & Sebastianelli, R. (2015). The relative importance of e-tailer website attributes on the likelihood of online purchase. Internet Research, 25(2), 169-183.
• Thet, T. T., Na, J. C., & Khoo, C. S. G. (2010). Aspect-based sentiment analysis of movie reviews on discussion boards. Journal of Information Science, 36(6), 823-848.
• Waheed, M., Kaur, K., & Qazi, A. (2016). Students’ perspective on knowledge quality in eLearning context: a qualitative assessment. Internet Research, 26(1), 120-145. 
• White, A., & Schmidt, K. (2005). Systematic literature reviews. Complementary therapies in medicine, 13(1), 54-60.
• Wu, W., Li, H., Wang, H., & Zhu, K. Q. (2012). Probase: A probabilistic taxonomy for text understanding. Paper presented at the Proceedings of the 2012 international conference on Management of Data.
• Xu, K., Liao, S. S., Li, J., & Song, Y. (2011). Mining comparative opinions from customer reviews for Competitive Intelligence. Decision Support Systems, 50(4), 743-754.
• Xu, K. S. J., Wang, W., Ren, J., Xu, J. S. Y., Liu, L., & Liao, S. (2011). Classifying Consumer Comparison Opinions to Uncover Product Strengths and Weaknesses.
• International Journal of Intelligent Information Technologies (IJIIT), 7(1), 1-14. 
• Ye, Q., Zhang, Z., & Law, R. (2009). Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications, 36(3, Part 2), 6527-6535.
• Yu, H., & Hatzivassiloglou, V. (2003). Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. Paper presented at the Proceedings of the 2003 conference on Empirical methods in natural language processing.

https://www.emerald.com/insight/content/doi/10.1108/IntR-04-2016-0086/full/html

Why Valerie Solana Shot Andy Warhol

.

On This Day: Valerie Solanas Shoots Andy Warhol

On June 3, 1968, radical feminist Valerie Solanas shot pop culture artist Andy Warhol in his Manhattan studio. Though he was initially being pronounced dead, Warhol survived.
Andy Warhol Nearly Killed

Valerie Solanas, angered that Andy Warhol had lost a script of hers, made an afternoon visit to Warhol’s Midtown Manhattan studio, known as The Factory, with a .32 revolver stashed in a brown paper bag. Warhol, accompanied by his boyfriend, Jed Johnson, and art critic Mario Amaya, saw Solanas outside The Factory and invited her in.

Once inside, Solanas pulled out the gun. The Village Voice’s Howard Smith described: “Warhol turned and saw the gun. ‘Valerie,’ he yelled. ‘Don't do it! No! No!’ She fired three shots, and Warhol fell to the floor.”

Solanas then shot Amaya and aimed at Warhol’s manager, Fred Hughes, but her gun jammed and she fled. She later turned herself in to rookie traffic cop William Shemalix, handing him her gun and saying she had shot Andy Warhol “because he had too much control of my life.”

After the shooting, Warhol was taken to Columbus Hospital and was pronounced dead, but doctors resuscitated him and he survived after emergency surgery.

Source: http://www.findingdulcinea.com/news/on-this-day/May-June-08/On-this-Day--Valerie-Solanas-Shoots-Andy-Warhol.html
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Approaches to Cross-Domain Sentiment Analysis: A Systematic Literature Review

 

Abstract

A sentiment analysis has received a lot of attention from researchers working in the fields of natural language processing and text mining. 

However, there is a lack of annotated data sets that can be used to train a model for all domains, which is hampering the accuracy of sentiment analysis. 

Many research studies have attempted to tackle this issue and to improve cross-domain sentiment classification. 

In this paper, we present the results of a comprehensive systematic literature review of the methods and techniques employed in a cross-domain sentiment analysis. 

We focus on studies published during the period of 2010-2016. 

From our analysis of those works, it is clear that there is no perfect solution. 

Hence, one of the aims of this review is to create a resource in the form of an overview of the techniques, methods, and approaches that have been used to attempt to solve the problem of cross-domain sentiment analysis in order to assist researchers in developing new and more accurate techniques in the future.

https://ieeexplore.ieee.org/abstract/document/7891035

REFERENCES

[1] A. Immonen, P. Pääkkönen, and E. Ovaska, ‘‘Evaluating the Quality

of Social Media Data in Big Data Architecture,’’ IEEE Access, vol. 3,

pp. 2028–2043, 2015.

[2] D. Jiang, X. Luo, J. Xuan, and Z. Xu, ‘‘Sentiment computing for the

news event based on the social media big data,’’ IEEE Access, vol. 5,

pp. 2373–2382, 2016.

[3] M. N. Injadat, F. Salo, and A. B. Nassif, ‘‘Data mining techniques in social

media: A survey,’’ Neurocomputing, vol. 214, pp. 654–670, Nov. 2016.

[4] T. A. A. Al-Moslmi, Machine Learning and Lexicon-Based Approach for

Arabic Sentiment Analysis. Bangi, Malaysia: Fakulti Teknologi & Sains

Maklumat/Institut, 2014.

[5] N. Omar, M. Albared, T. Al-Moslmi, and A. Al-Shabi, ‘‘A comparative

study of feature selection and machine learning algorithms for arabic

sentiment classification,’’ in Information Retrieval Technology. Springer,

2014, pp. 429–443.

[6] M. Bouazizi and T. Ohtsuki, ‘‘A pattern-based approach for sarcasm detec-

tion on twitter,’’ IEEE Access, vol. 4, pp. 5477–5488, 2016.

[7] F. Bertola and V. Patti, ‘‘Ontology-based affective models to organize

artworks in the social semantic Web,’’ Inf. Process. Manage., vol. 52, no. 1,

pp. 139–162, 2016.

[8] G. Vinodhini and R. M. Chandrasekaran, ‘‘A sampling based sentiment

mining approach for e-commerce applications,’’ Inf. Process. Manag.,

vol. 53, no. 1, pp. 223–236, 2016.

[9] R. Piryani, D. Madhavi, and V. K. Singh, ‘‘Analytical mapping of opinion

mining and sentiment analysis research during 2000âĂŞ2015,’’ Inf. Pro-

cess. Manag., vol. 53, no. 1, pp. 122–150, 2016.

[10] T. Al-Moslmi, S. Gaber, A. Al-Shabi, M. Albared, and N. Omar, ‘‘Feature

selection methods effects on machine learning approaches in malay senti-

ment analysis,’’ in Proc. 1st ICRIL-Int. Conf. Inno. Sci. Technol. (IICIST),

2015, pp. 1–2.

[11] N. Omar, M. Albared, A. Al-Shabi, and T. Al-Moslmi, ‘‘Ensemble of

classification algorithms for subjectivity and sentiment analysis of ara-

bic customers ‘reviews,’’ Int. J. Adv. Comput. Technol., vol. 14, no. 5,

pp. 77–85, 2013.

[12] Z. Liu, S. Liu, L. Liu, J. Sun, X. Peng, and T. Wang, ‘‘Sentiment recognition

of online course reviews using multi-swarm optimization-based selected

features,’’ Neurocomputing, vol. 185, pp. 11–20, Mar. 2016.

[13] T. Al-Moslmi, M. Albared, A. Al-Shabi, N. Omar, and S. Abdullah,

‘‘Arabic senti-lexicon: Constructing publicly available language resources

for Arabic sentiment analysis,’’ J. Inf. Sci., vol. 23, p. 16555, Feb. 2017.

[14] F. Wu, Y. Huang, and Y. Song, ‘‘Structured microblog sentiment clas-

sification via social context regularization,’’ Neurocomputing, vol. 175,

pp. 599–609, Jun. 2016.

[15] K. Dashtipour et al., ‘‘Multilingual sentiment analysis: State of the art and

independent comparison of techniques,’’ Cognit. Comput., vol. 8, no. 4,

pp. 1–15, 2016.

[16] S. Keele, ‘‘Guidelines for performing systematic literature reviews in soft-

ware engineering,’’ Dept. Comput. Sci., Univ. f Durham, Durham, U.K.,

Tech. Rep. Ver.2.3, 2007.

[17] J. Carrillo de Albornoz, L. Plaza, and P. Gervás, ‘‘A hybrid approach to

emotional sentence polarity and intensity classification,’’ in Proc. 14th

Conf. Comput. Natural Lang. Learn., 2010, pp. 153–161.

[18] I. Maks and P. Vossen, ‘‘A lexicon model for deep sentiment analysis

and opinion mining applications,’’ Decision Support Syst., vol. 53, no. 4,

pp. 680–688, Nov. 2012.

[19] S. J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen, ‘‘Cross-domain sentiment

classification via spectral feature alignment,’’ in Proc. 19th Int. Conf. World

Wide Web, vol. 10. 2010, p. 751.

[20] J. Blitzer, M. Dredze, and F. Pereira, ‘‘Biographies, bollywood, boom-

boxes and blenders: Domain adaptation for sentiment classification,’’ in

Proc. ACL, vol. 7. 2007, pp. 440–447.

[21] D. Bollegala, T. Mu, and J. Y. Goulermas, ‘‘Cross-domain sentiment

classification using sentiment sensitive embeddings,’’ IEEE Trans. Knowl.

Data Eng., vol. 28, no. 2, pp. 398–410, Feb. 2016.

[22] J. Liang, K. Zhang, X. Zhou, Y. Hu, J. Tan, and S. Bai, ‘‘Leveraging

latent sentiment constraint in probabilistic matrix factorization for cross-

domain sentiment classification,’’ Proc. Comput. Sci., vol. 80, pp. 366–375,

Mar. 2016.

[23] N. X. Bach, V. T. Hai, and T. M. Phuong, ‘‘Cross-domain sentiment

classification with word embeddings and canonical correlation analysis,’’

in Proc. 7th Symp. Inf. Commun. Technol., 2016, pp. 159–166.

[24] Y. Zhang, X. Hu, P. Li, L. Li, and X. Wu, ‘‘Cross-domain sentiment

classification-feature divergence, polarity divergence or both?’’ Pattern

Recognit. Lett., vol. 65, pp. 44–50, Jun. 2015.

[25] M. Franco-Salvador, F. L. Cruz, J. A. Troyano, and P. Rosso,

‘‘Cross-domain polarity classification using a knowledge-enhanced meta-

classifier,’’ Knowl.-Based Syst., vol. 86, pp. 46–56, Jun. 2015.

[26] H. Hammer, A. Yazidi, A. Bai, and P. Engelstad, ‘‘Building domain specific

sentiment lexicons combining information from many sentiment lexicons

and a domain specific corpus,’’ Comput. Sci. Appl., vol. 456, pp. 205–216,

Dec. 2015.

[27] G. Zhou, Y. Zhou, X. Guo, X. Tu, and T. He, ‘‘Cross-domain senti-

ment classification via topical correspondence transfer,’’ Neurocomputing,

vol. 159, pp. 298–305, Dec. 2015.

[28] R. Zhao and K. Mao, ‘‘Supervised adaptive-transfer PLSA for cross-

domain text classification,’’ in Proc. IEEE Int. Conf. Data Mining Work-

shop, Jan. 2014, pp. 259–266.

[29] C. Lin, Y. Lee, C. Yu, and H. Chen, ‘‘Exploring ensemble of models

in taxonomy-based cross-domain sentiment classification,’’ in Proc. 23rd

ACM Int. Conf. Conf. Inf. Knowl. Manage.-(CIKM), 2014, pp. 1279–1288.

[30] Y. Tsai, R. T. Tsai, C. Chueh, and S. Chang, ‘‘Cross-domain opinion word

identification with query-by-committee active learning,’’ in Technologies

and Applications of Artificial Intelligence. Cham, Switzerland: Springer,

2014, pp. 334–343.

[31] A. Tsakalidis, ‘‘An ensemble model for cross-domain polarity classifica-

tion on twitter,’’ in Web Information Systems Engineering. Cham, Switzer-

land: Springer, 2014, pp. 168–177.

[32] F. Bisio, P. Gastaldo, C. Peretti, R. Zunino, and E. Cambria, ‘‘Data intensive

review mining for sentiment classification across heterogeneous domains,’’

in Proc. IEEE/ACM Int. Conf. Adv. Social Netw. Anal. Mining, vol. 13.

Aug. 2013, pp. 1061–1067.

[33] P. Yang, W. Gao, Q. Tan, and K.-F. Wong, ‘‘A link-bridged topic model

for cross-domain document classification,’’ Inf. Process. Manage., vol. 49,

no. 6, pp. 1181–1193, Nov. 2013.

[34] D. Bollegala, D. Weir, and J. Carroll, ‘‘Cross-domain sentiment classifi-

cation using a sentiment sensitive thesaurus,’’ IEEE Trans. Knowl. Data

Eng., vol. 25, no. 8, pp. 1719–1731, Aug. 2013.

[35] R. Xia, C. Zong, X. Hu, and E. Cambria, ‘‘Feature ensemble plus sample

selection: Domain adaptation for sentiment classification,’’ IEEE Intell.

Syst., vol. 28, no. 3, pp. 10–18, May 2013.

[36] Z. Zhu, D. Dai, Y. Ding, J. Qian, and S. Li, ‘‘Employing emotion keywords

to improve cross-domain sentiment classification,’’ in Proc. Chin. Lexical

Semantics, 2013, pp. 64–71.

[37] Y. He, C. Lin, W. Gao, and K.-F. Wong, ‘‘Dynamic joint sentiment-topic

model,’’ ACM Trans. Intell. Syst. Technol., vol. 5, no. 1, p. 6, 2013.

[38] S. Li, Y. Xue, Z. Wang, and G. Zhou, ‘‘Active learning for cross-domain

sentiment classification,’’ in Proc. 23rd Int. Joint Conf. Artif. Intell., 2013,

pp. 2127–2133.

[39] N. Ponomareva and M. Thelwall, ‘‘Semi-supervised vs. cross-domain

graphs for sentiment analysis,’’ in Proc. RANLP, 2013, pp. 571–578.

[40] B. Ohana, S. J. Delany, and B. Tierney, ‘‘A case-based approach to cross

domain sentiment classification,’’ in Lecture Notes in Computer Science,

vol. 7466. Cham, Switzerland: Springer, 2012, pp. 284–296.

[41] R. Remus, ‘‘Domain adaptation using domain similarity- and domain

complexity-based instance selection for cross-domain sentiment analy-

sis,’’ in Proc.-12th IEEE Int. Conf. Data Mining Workshops, Jun. 2012,

pp. 717–723.

[42] N. Ponomareva and M. Thelwall, ‘‘Biographies or blenders: Which

resource is best for cross-domain sentiment analysis?’’ in Lecture Notes in

Computer Science, vol. 7181. Cham, Switzerland: Springer, 2012, pp. 488–

499.

[43] N. Ponomareva and M. Thelwall, ‘‘Do neighbours help?: An exploration

of graph-based algorithms for cross-domain sentiment classification,’’ in

Proc. Joint Conf. Empirical Methods Natural Lang. Process. Comput.

Natural Lang. Learn., Jul. 2012, pp. 655–665.

[44] S. D. Roy, T. Mei, W. Zeng, and S. Li, ‘‘SocialTransfer: Cross-domain

transfer learning from social streams for media applications,’’ in Proc. 20th

ACM Int. Conf. Multimedia, 2012, pp. 649–658.

[45] Q. Wu and S. Tan, ‘‘A two-stage framework for cross-domain sentiment

classification,’’ Expert Syst. Appl., vol. 38, no. 11, pp. 14269–14275,

2011.

[46] Y. He, C. Lin, and H. Alani, ‘‘Automatically extracting polarity-bearing

topics for cross-domain sentiment classification,’’ in Proc. 49th Annu.

Meeting, Jun. 2011, pp. 123–131.

[47] X. Glorot, A. Bordes, and Y. Bengio, ‘‘Domain adaptation for large-scale

sentiment classification: A deep learning approach,’’ in Proc. 28th Int.

Conf. Mach. Learn., 2011, pp. 513–520.

[48] S. Li and C. Zong, ‘‘Multi-domain adaptation for sentiment classification:

Using multiple classifier combining methods,’’ in Proc. Int. Conf. Natural

Lang. Process. Knowl. Eng. (NLP-KE), 2008, pp. 1–8.

[49] G.-R. Xue, W. Dai, Q. Yang, and Y. Yu, ‘‘Topic-bridged PLSA for cross-

domain text classification,’’ in Proc. 31st Annu. Int. ACM SIGIR Conf. Res.

Develop. Inf. Retrieval, 2008, pp. 627–634.

[50] B. Wang, J. Tang, W. Fan, S. Chen, Z. Yang, and Y. Liu, ‘‘Heterogeneous

cross domain ranking in latent space,’’ in Proc. 18th ACM Conf. Inf. Knowl.

Manage., 2009, pp. 987–996.

[51] F. Zhuang et al., ‘‘Collaborative dual-PLSA: Mining distinction

and commonality across multiple domains for text classification,’’

in Proc. 19th ACM Int. Conf. Inf. Knowl. Manage., 2010,

pp. 359–368.

[52] K. Lang, ‘‘NewsWeeder: Learning to filter netnews,’’ in Proc. 12th Int.

Conf. Mach. Learn., 1995, pp. 331–339.

[53] D. D. Lewis. (1997). Reuters-21578 Text Categorization Test Col-

lection, Distribution 1.0. [Online]. Available: http://www.research.att.

com/lewis/reuters21578.html

[54] A. Go, R. Bhayani, and L. Huang, ‘‘Twitter sentiment classification

using distant supervision,’’ CS224N Project Rep., Stanford, vol. 1, no. 2,

pp. 12–18, 2009.

[55] M. Speriosu, N. Sudan, S. Upadhyay, and J. Baldridge, ‘‘Twitter polar-

ity classification with label propagation over lexical links and the fol-

lower graph,’’ in Proc. 1st Workshop Unsupervised Learn. (NLP), 2011,

pp. 53–63.

[56] H. Wang, Y. Lu, and C. Zhai, ‘‘Latent aspect rating analysis on review text

data: A rating regression approach,’’ in Proc. 16th ACM SIGKDD Int. Conf.

Knowl. Discovery Data Mining, 2010, pp. 783–792.

[57] A. K. McCallum, K. Nigam, J. Rennie, and K. Seymore, ‘‘Automating the

construction of Internet portals with machine learning,’’ Inf. Retr., vol. 3,

no. 2, pp. 127–163, 2000.

[58] B. Pang, L. Lee, and S. Vaithyanathan, ‘‘Thumbs up?: Sentiment classifi-

cation using machine learning techniques,’’ in Proc. ACL-Conf. Empirical

Methods Natural Lang., vol. 10. 2002, pp. 79–86.

[59] S. Baccianella, A. Esuli, and F. Sebastiani, ‘‘Multi-facet rating of prod-

uct reviews,’’ in Advances in Information Retrieval. Cham, Switzerland:

Springer, 2009, pp. 461–472.

[60] N. Jindal and B. Liu, ‘‘Opinion spam and analysis,’’ in Proc. Int. Conf. Web

Search Data Mining, 2008, pp. 219–230.

[61] J. Blitzer, R. McDonald, and F. Pereira, ‘‘Domain adaptation with struc-

tural correspondence learning,’’ in Proc. Conf. Empirical Methods Natural

Lang. Process., 2006, pp. 120–128.

[62] S. Li and C. Zong, ‘‘Multi-domain sentiment classification,’’ in Proc. 46th

Annu. Meeting Assoc. Comput. Linguistics Human Lang. Technol., Short

Papers, 2008, pp. 257–260.

[63] H. Guo, H. Zhu, Z. Guo, X. Zhang, X. Wu, and Z. Su, ‘‘Domain adaptation

with latent semantic association for named entity recognition,’’ in Proc.

Human Lang. Technol., Annu. Conf. North Amer. Chapter Assoc. Comput.

Linguistics, 2009, pp. 281–289.

[64] P. Wang, C. Domeniconi, and J. Hu, ‘‘Using wikipedia for co-clustering

based cross-domain text classification,’’ in Proc. 8th IEEE Int. Conf. Data

Mining (ICDM), Dec. 2008, pp. 1085–1090.

[65] G. Paltoglou and M. Thelwall, ‘‘A study of information retrieval weighting

schemes for sentiment analysis,’’ in Proc. 48th Annu. Meeting Assoc.

Comput. Linguistics, 2010, pp. 1386–1395.

[66] N. Jakob and I. Gurevych, ‘‘Extracting opinion targets in a single-and

cross-domain setting with conditional random fields,’’ in Proc. Conf.

Empirical Methods Natural Lang. Process., Oct. 2010, pp. 1035–1045.

[67] F. Huang and A. Yates, ‘‘Exploring representation-learning approaches to

domain adaptation,’’ in Proc. Workshop Domain Adaptation Natural Lang.

Process., 2010, pp. 23–30.

[68] Y. Bao, N. Collier, and A. Datta, ‘‘A partially supervised cross-collection

topic model for cross-domain text classification,’’ in Proc. 22nd ACM Int.

Conf. Conf. Inf. Knowl. Manage., 2013, pp. 239–248.

[69] D. C. T. Hofmann, ‘‘The missing link-a probabilistic model of docu-

ment content and hypertext connectivity,’’ in Proc. Conf. Adv. Neural Inf.

Process. Syst., 2001, pp. 430–436.

[70] R. Serafin and B. Di Eugenio, ‘‘FLSA: Extending latent semantic analysis

with features for dialogue act classification,’’ in Proc. 42nd Annu. Meeting

Assoc. Comput. Linguistics, 2004, p. 692.

[71] A. P. Dempster, N. M. Laird, and D. B. Rubin, ‘‘Maximum likelihood from

incomplete data via the EM algorithm,’’ J. Roy. Statist. Soc. Ser. B, 1977,

pp. 1–38.

[72] T. Li, V. Sindhwani, C. Ding, and Y. Zhang, ‘‘Knowledge transformation

for cross-domain sentiment classification,’’ in Proc. 32nd Int. ACM SIGIR

Conf. Res. Develop. Inf. Retrieval, 2009, pp. 716–717.

[73] L. Li, X. Jin, and M. Long, ‘‘Topic correlation analysis for cross-domain

text classification,’’ in Proc. AAAI, 2012, p. 12.

[74] T. Joachims, ‘‘Transductive inference for text classification using support

vector machines,’’ in Proc. ICML, vol. 99. 1999, pp. 200–209.

[75] Q. Wu, S. Tan, H. Zhai, G. Zhang, M. Duan, and X. Cheng, ‘‘Senti-

Rank: Cross-domain graph ranking for sentiment classification,’’ in Proc.

IEEE/WIC/ACM Int. Joint Conf. Web Intell. Intell. Agent Technol., vol. 1.

2009, pp. 309–314.

[76] J. Yu and J. Jiang, ‘‘Learning sentence embeddings with auxiliary tasks for

cross-domain sentiment classification,’’ in Proc. Conf. Empirical Methods

Natural Lang. Process., 2016, pp. 236–246.

[77] D. M. Blei and M. I. Jordan, ‘‘Modeling annotated data,’’ in Proc.

26th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2003,

pp. 127–134.

[78] J. Kranjc et al., ‘‘Active learning for sentiment analysis on data streams:

Methodology and workflow implementation in the ClowdFlows platform,’’

Inf. Process. Manage., vol. 51, no. 2, pp. 187–203, 2015.

[79] B. Settles, Active Learning Literature Survey. Madison, WI, USA:

Univ. Wisconsin, 2010.

[80] F. Olsson, ‘‘A literature survey of active machine learning in the context of

natural language processing,’’ in Proc. SODA, 2009, p. 36.

[81] D. Nozza, E. Fersini, and E. Messina, ‘‘Deep learning and ensemble

methods for domain adaptation,’’ in Proc. IEEE 28th Int. Conf. Tools Artif.

Intell. (ICTAI), Nov. 2016, pp. 184–189.

[82] M. Long, J. Wang, Y. Cao, J. Sun, and S. Y. Philip, ‘‘Deep learning of

transferable representation for scalable domain adaptation,’’ IEEE Trans.

Knowl. Data Eng., vol. 28, no. 8, pp. 2027–2040, Feb. 2016.

[83] T. Hofmann, ‘‘Unsupervised learning by probabilistic latent semantic anal-

ysis,’’ Mach. Learn., vol. 42, no. 1, pp. 177–196, Jan. 2001.

[84] P. Sanju and T. T. Mirnalinee, ‘‘Construction of enhanced sentiment sensi-

tive thesaurus for cross domain sentiment classification using wiktionary,’’

in Proc. 3rd Int. Conf. Soft Comput. Problem Solving, 2014, pp. 195–206.

[85] S. M. Jiménez-Zafra et al., ‘‘Domain adaptation of polarity lexicon com-

bining term frequency and bootstrapping,’’ in Proc. NAACL-HLT, 2016,

pp. 137–146.

[86] A. Aamodt and E. Plaza, ‘‘Case-based reasoning: Foundational issues,

methodological variations, and system approaches,’’ AI Commun., vol. 7,

no. 1, pp. 39–59, 1994.

[87] X. Zhu, J. Lafferty, and R. Rosenfeld, Semi-Supervised Learning With

Graphs. Pittsburgh, PA, USA: Carnegie Mellon Univ., 2005.

[88] X. Zhu and Z. Ghahramani, Learning From Labeled and Unlabeled Data

With Label Propagation. Seattle, WA, USA: Semantic Scholar, 2002.

[89] A. B. Goldberg and X. Zhu, ‘‘Seeing stars when there aren’t many stars:

Graph-based semi-supervised learning for sentiment categorization,’’ in

Proc. 1st Workshop Graph Based Methods Natural Lang. Process., 2006,

pp. 45–52.

[90] R. Navigli and S. P. Ponzetto, ‘‘BabelNet: The automatic construction,

evaluation and application of a wide-coverage multilingual semantic net-

work,’’ Artif. Intell., vol. 193, pp. 217–250, Dec. 2012.

[91] D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, ‘‘Preferred reporting

items for systematic reviews and meta-analyses: The PRISMA statement,’’

Ann. Internal Med., vol. 151, no. 4, pp. 264–269, 2009.

The Effects of Emoji in Sentiment Analysis

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Abstract: This study investigates the usage of Emoji characters on social networks and the effects of Emoji in  text  mining and sentiment  analysis. As  it provides live access  to  text  based  public opinions,  we  chose Twitter  as our  information  source  in  our  analysis.  We  collected  text  data  for  some  global  positive  and negative events to analyze the impact of Emoji characters in sentiment analysis. In our analysis, we noticed that  the  utilization  of  Emoji  characters  in  sentiment  analysis  results  in  higher  sentiment  scores. Furthermore, we observed that the usage of Emoji characters in sentiment analysis appeared to have higher impact on overall sentiments of the positive opinions in comparison to the negative opinions.    Key words: Emoji, opinion mining, sentiment analysis, twitter.
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https://www.researchgate.net/publication/320446679_The_Effects_of_Emoji_in_Sentiment_Analysis