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  • International Journal of Advanced Research in Engineering RESEARCH IN ENGINEERING INTERNATIONAL JOURNAL OF ADVANCED and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 4, Issue 7, November - December 2013, pp. 176-182 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2013): 5.8376 (Calculated by GISI) www.jifactor.com IJARET ©IAEME AN OVERVIEW OF OPINION MINING TECHNIQUES Dr. Jamshed Siddiqui Department of Computer Science, Aligarh Muslim University, Aligarh, U.P. ABSTRACT The world with an intense increase in the changing technologies and due to the rapid growth in the Internet, facing changes dramatically and created the scenario that life style of an individual has also got changed. Users use Internet tools like blogs, social networking sites etc. to share their views and opinions for various daily life issues that vary from on-line marketing and reviewing the product to the election campaign and views of a voter for their electoral candidate. Opinion Mining is required for such situation to infer the correct results and predict the future behavior. In this paper, a short review to various opinion mining techniques is performed with tables depicting the contribution in recent times with figures illustrations. Various techniques used, Corpus description and basic concepts are also discussed. This work may be useful for the researcher to get a background of opinion mining and finding future trends. Keywords: Blogs, Opinion Mining, Internet, Social Networking, Corpus. I. INTRODUCTION The celerity in the growth of the Internet has a diverse effect in the daily life of a common man. The world with an intense increase in the changing technologies and due to the rapid growth in the Internet, facing changes dramatically and created the scenario that life style of an individual has also got changed. Because Internet is being used commonly, the social lives are also affected. Users use Internet tools like blogs, social networking sites etc. to share their views and opinions for various daily life issues that vary from on-line marketing and reviewing the product to the election campaign and views of a voter for their electoral candidate. Opinions of one influence other and play a key role in deciding the behavior. Almost all of us take help from other ideas and views to decide own way to proceed, individuals would like to know the opinions of their family and friends, organizations use opinion polls, conduct surveys and hire consultants for making their strategies. The need of opinion 176
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME mining emerges in such a situation to infer the right choice and to get benefited by the other experiences and views. In last few decades, the opinion mining has gained much popularity [1]. Opinion mining can be considered as a computational study of the opinions [2]. The sources of the opinion may be the social networking sites like Facebook and twitter, feedback from emails of the employee of an organization, opinions in news articles, blogs, product web sites where thousands of user generated free text are available, etc. Inferring the true aspect of an opinion is not an easy task, sometimes a very confusing state is created. The same object may be perceived differently by different people. For an example let us consider Mahatma Gandhi, for India he is non-violent, father of the nation but British ruler would refer him as their enemy. So the main task in opinion finding is to consider all these aspect and to infer right thing according to the situation for the target customer that may be a newspaper reader and an online-customer even a voter who wishesto vote for his choice of candidate. Rest of the paper is organized as follows. The second section reviews the literature and in the third section the different aspects of data mining is elaborated. Fourth section concludes the summary. II. LITERATURE REVIEW Data Mining is one of the esteemed branch in computer science which was started initially for business purposes[3], with timeits area of research is going to spread over medical sciences, scientific research and social networking.[4]. Opinion mining is one of the most emergingbranch of the data mining widely being used worldwide, interchangeably termed as sentiment analysis[2]. Themining of direction-based was first proposed by Hearst and Wieb that may include biases [5]. Opinion mining is done in text analysis and is similar as to find positive negative text, that can also be done by supervised and unsupervised learning as well [6]. Reviews and opinions of user generated freetext plays an important role in sentiment analysis [7] the contribution in identifying review in heterogeneous groups of text was made by Finn[8], it simply implies subjectivity in [9] tried to overcome the difficulties in getting the similarities and differences in the connoted word. The technicality in the similarity between two words was also described by [10]. In [11, 12, 13] authors tried to give a solution for overly specific problem in subjectivity identification. The manual annotation has two main issues; time consuming and expensive. [14, 15, 16] for linguistic corpora, a research based upon corpus is very useful. MPQA is one of these kinds of existing corpus. But annotation was not based upon sentence level, but only word level[17]. In [18] the contribution of Pang corpus generation is also discussed.For1 million English word, an international corpus of English is supposed to be launched soon [19], A detail discussion of opinion mining is performed in [20]; subtask in TREC blog track is one of the examples of opinion retrieval. Feature based opinion summarization of reviews was suggested byHu and Liu [21]. 177
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME 1. Yi Fangy, LuoSiy, Naveen Somasundaramy, ZhengtaoYuz 2012 Basis of Research Cross-prospective Query Topic, Topic Model Text Collections 2. Daniel E. O'Leary 2011 Reviewing Blogs Blogs Extensive review on the topic 3. PawelSobkowicz, Michael Kaschesky, Guillaume Bouchard 2012 Opinion formation framework previous research, online opinions emerge diffusion emotion and opinion detection, model of opinion network, information flow modeling, agent base simulation 4. KaiquanXu, Stephen Shaoyi Liao, Jiexun Li, Yuxia Song 2010 Graphical Model to extract and visualize customer generated reviews extract customer reviews, visualization, analyze customer generated data 5. M. Eirinaki, S. pisal, J. Singh 2011 sentiment analysis and semantic orientation algorithm reviews, search engine Analysis of sentiments, Semantic Orientation, Opinion search engine. 6. AmnaAsmi and TankyoIshaya 2012 Auto generation of corpus user generated content, WorldNet generation of corpus 7. S.S. Patil and A.P. Chaudhary 2012 SVM classification Corpus, reviews Emotion classification in 6 categories 8. Kushal Dave, Steve Lawrence, David M. Pennock 2003 Reviews Classifiers Product Reviews Distinguishing positive and negative reviews , grouping sentiments into attribute Sl.No. Authors Year Proposed Model Contribution Opinion of individual perspective on the topic, Quantifying opinion differences III. OPINION MINING TECHNIQUE The term opinion mining is similar to the sentiment analysis and both are widely used in academia, though in industry sentiment analysis is used extensively[2]. In general, there are two types of opinions: 1) Regular opinions 2) Comparative opinions 178
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME In regular opinion, sentiments on some target entities are expressed; simply they are either negative or positive expressions of their opinions for some specific aspects. There are two sub categories: a) Direct opinion b) Indirect opinion Direct opinion involves direct statement like, “the book is very interesting and knowledgegiving”, indirect statement involves indirect expressions like, “after reading the book, I fill the bucket of my knowledge”. In comparative opinions, two are more entities are compared with each other, e.g. “MCQ physics by Upkar is better than Objective Physics by Sanjeev Gupta.” The term entity refers to products in general, this product can be a person, an organization or even a topic of discussion. The entity ‘product’ is associated with a set of attributes called node if represented graphically. One can express opinion on any attribute of the node. Depicted in figure 1, university is an organization that has several branches, here branches refer faculties. And these branches have nodes, for our example these are departments. These can be considered as the components of the attributes university. So concluding about university i.e. entity or attribute may need knowing the opinion about the department i.e. components. The term aspect or feature refers to both attributes and components. University Faculty Faculty Faculty Faculty (Arts) (Soc. Sc.) (Science) (Medical) ) Departments Hindi English Physics Computer Urdu Geology Figure 1. Opinion Mining for different branches of an item IV. CRITERIA FOR OPINION MINING The opinion mining can be categorized on several bases, extracting features from opinion and inferring the conclusion from those opinions is a very important aspect in the field of sentiment analysis. Therefore feature based opinion mining is very important and plays a vital role in sentiment analysis. Feature based opinion mining involves following steps: 179
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME IV. I Reviews Collection The collections of reviews are also very important. The source for these reviews decides the content and accordingly the decision is made. Blogs, Social networking site, News, emails, Products web sites etc. are the main sources in the Internet for the reviews collections. IV. II Feature Extraction Features extraction is one of the important task in opinion mining, in [s] gave a formulation based method to extract opinion. Though the work was done manually, however features extraction method is also discussed in [22], where author discussed two types of features: a) Implicit Feature b) Explicit Feature Implicit features involve those features of the products which users give in some specific form, like in adjective form. The sentences in the reviews as described below could be implicit. The book is too huge to read Explicit features involve the features of a product described in Noun form, like: The book is precise and interesting There are features pruning as well so that to remove unnecessary features not essential and probably may be incorrect. A diagrammatic representation of opinion mining is depicted in the figure 2. Mining Review/Blog User Sentiment Inference Corpus Figure 2. Overview of Opinion Mining Technique IV. III Inferring Results After the extraction of features, the inference of conclusion is the major task that depends upon the experimental work done and the parameters and algorithms developed. There are various statistical parameters and different classifiers that is usualy used for the purpose. In [patiliaeme], they discussed various classifiers, Support Vector Machine (SVM), Vector Machine (VM) and NAVIE etc. 180
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME V. CONCLUSION We usually take help from other ideas and views to decide own way to proceed, individuals would like to know the opinions of their family and friends, organizations use opinion polls, conduct surveys and hire consultants for making their strategies. The need of opinion mining emerges in such a situation to infer the right choice and to get benefited by the other experiences and views. In this paper the importance of user generated reviews including the description of existing corpus and technologies used to extract features from these reviews are discussed. Different types of opinion mining and techniques are also discussed. In future these work would help the research to find the coming challenges and to contribute with a high note in the filed of opinion mining. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] AmnaAsmi, and TankyoIshaya, A framework for automated corpus generation for semantic sentiment analysis, proceeding of the world congress on engineering vol. 1, 2012. Bing Liu, Sentiment Analysis and Opinion Mining, (San Francisco: Morgan & Claypool Publishers, 2012). A.M. Patel, A.R.Patel and H.R.Patel, A Comparative analysis of data mining tools for performance mapping of WLAN data, 4(2), 241-251. R. Manickam, An Analysis of data mining: past, present and future, 3(1), 1-9. Abbasi, A., Chen, H. and Salem, A. (2008) Sentiment analysis inmultiple languages: Feature selection for opinion classification inWeb forums. ACM Trans. Inf. Syst., 26(3), 2005, 1-34. M. J. Silva et al., The Design of OPTIMISM, an Opinion Mining System for Portuguese Politics. In: New Trends in Artificial Intelligence: Proceedings of EPIA 2009 - Fourteenth PortugueseConference on Artificial Intelligence. Aveiro, Portugal. Universidade de Aveiro, 2009, 565-576. B. Pang, L. Lee, and S. Vaithyanathan, Thumbs up?: sentiment classification using machine learning techniques. In: The ACL-02 conference on Empirical methods in natural language processing Philadelphia, PA, USA. Association for Computational Linguistics, 2002, 79-86. Aidan Finn, Nicholas Kushmerick, and Barry Smyth, Genre classification and domain transfer for information filtering. In Fabio Crestani, Mark Girolami, Proceedings of ECIR02, 24th European Colloquium on Information Retrieval Research, Glasgow, UK. Springer Verlag, Heidelberg, DE, 2002 VasileiosHatzivassiloglou and Kathleen R. McKeown, Predicting the semantic orientation of adjectives.In Proceedings of the 35th Annual Meeting of ACL, 1997. P.D. Turney and M.L. Littman. Unsupervised learning of semantic orientation from a hundred-billion-word corpus. Technical Report ERB-1094, National Research Council Canada, Institute for Information Technology, 2002. Deepak Ravichandran and Eduard Hovy. Learning surface text patterns for a question answering system. In ACL Conference, 2002. Ellen Riloff. Automatically generating extraction patterns from untagged text.In Proceedings of AAAI/IAAI, Vol. 2, 1996 1044–1049. JanyceWiebe, Theresa Wilson, and Matthew Bell.Identifying collocations for recognizing opinions.In Proceedings of ACL/EACL 2001 Workshop on Collocation. David Holtzmann. Detecting and tracking opinions in on-line discussions. In UCB/SIMS Web Mining Workshop, 2001. Dekang Lin. Automatic retrieval and clustering of similar words. In Proceedings of COLING-ACL, 1998, 768–774. JanyceWiebe. Learning subjective adjectives from corpora.In AAAI/IAAI, 2000, 735–740. 181
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME [17] Wiebe, J. and Riloff, E. Finding Mutual Benefit between Subjectivity Analysis and Information Extraction.Affective Computing, IEEE Transactions, Vol.99, 2011, 1-1. [18] M. J. Silva et al. (2009) The Design of OPTIMISM, an Opinion Mining System for Portuguese Politics. In: New Trends in Artificial Intelligence: Proceedings of EPIA Fourteenth Portuguese Conference on Artificial Intelligence. Aveiro, Portugal. Universidade deAveiro, 2009, 565-576. [19] Bhattacharyya, D., et al., Refine Crude Corpus for Opinion Mining. In: The First International Conference on Computational Intelligence, Communication Systems and Networks. Washington, DC, USA. IEEE Computer Society, 2009, 17-22. [20] B. Pang and L. Lee. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2 (1-2), 2008, 1-135. [21] Mingqing Hu and Bing Liu, Mining Opinion Features in Customer Reviews, American Association for Artificial Intelligence. 2004. [22] T. Saranya, Mining features and ranking products from online customer reviews, International Journal of Engineering Research & Technology, 10(2), 2013, 643-648. [23] R. Manickam, D. Boominath, V. Bhuvaneswari, “An Analysis of Data Mining: Past, Present and Future”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 1 - 9, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [27] Jamshed Siddiqui, “An Exploration of Total Quality Management and Supply Chain Management Enablers”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 6, 2013, pp. 212-218, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [24] Sandip S. Patil and Asha P. Chaudhari, “Classification of Emotions from Text using SVM Based Opinion Mining”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 330 - 338, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [25] Jamshed Siddiqui, “A Framework for ICT Adoption in Indian Smes: Issues and Challenges”, International Journal of Information Technology and Management Information Systems (IJITMIS), Volume 4, Issue 3, 2013, pp. 114 - 120, ISSN Print: 0976 – 6405, ISSN Online: 0976 – 6413. [26] M. Karthikeyan, M. Suriya Kumar and Dr. S. Karthikeyan, “A Literature Review on the Data Mining and Information Security”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 141 - 146, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 182