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

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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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