Sentiment Analysis: An Enhancement of Ontological-Based Using Hybrid Machine Learning Techniques

Muhammad Iqbal Abu Latiffi, Mohd Ridzwan Yaakub

Abstract


With the fast development of World Wide Web 2.0 has resulted in huge number of reviews where the consumers share their opinion about a variety of products in the websites, forum and social media such as Twitter and Instagram. For the organizations, they have to analyze customer’s behavior to find new market trends and insights. Sentiment analysis concept used to extract the positive, negative or neutral sentiment of the features from the unstructured data of product reviews. In this paper, we explore the techniques and tools used to enhance the ontology-based approach. Combination of ontology-based on Formal Concept Analysis (FCA) which a process of obtaining a formal ontology or a concept hierarchy from a group of objects with their properties and K-Nearest Neighbor (KNN) to classify the reviews. We believe with these techniques, we are able to view the strength and weakness of the product in more detail where the feature selection process will more be systematic and will result in the highest feature set.  


Keywords


sentiment analysis; ontology; Formal Concept Analysis; K-Nearest Neighbor

Full Text:

PDF

References


Ahmad, S.R., Yaakub, M.R. & Bakar, A.A. 2016. Detecting Relationship between Features and Sentiment Words using Hybrid of Typed Dependency Relations Layer and POS Tagging (TDR Layer POS Tags) Algorithm. International Journal on Advanced Science, Engineering and Information Technology 6(6): 1120.

Ali, F., Kwak, D., Khan, P., Islam, S.M.R., Kim, K.H. & Kwak, K.S. (2017). Fuzzy Ontology-based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling. Transportation Research Part C: Emerging Technologies 7 (2017): pp. 33-48.

Bhadane, C., Dalal, H. & Doshi, H. 2015. Sentiment analysis: Measuring opinions. Procedia Computer Science 45(C): pp. 808–814.

Choi, Y., Cardie, C., Riloff, E. & Patwardhan, S. 2005. Identifying sources of opinions with conditional random fields and extraction patterns. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT ’05(2003): pp. 355–362.

Dave, K., Dave, K., Lawrence, S., Lawrence, S., Pennock, D.M. & Pennock, D.M. 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. Proceedings of the 12th international conference on World Wide Web: pp. 519–528.

Doan, A., Madhavan, J., Domingos, P. & Halevy, A. 2004. Ontology matching: A machine learning approach. Science Vol. 13: pp. 1–20.

Ezhilarasi, R. & Minu, R.I. 2012. Automatic emotion recognition and classification. Procedia Engineering 38: pp. 21–26.

Haider, S. 2012. An Ontology Based Sentiment Analysis. A Case Study. Univesity of Skovde.

Hu, M. & Liu, B. 2004. Mining Opinion Features in Customer Reviews. 19th national conference on Artifical intelligence: pp. 755–760.

Kontopoulos, E., Berberidis, C., Dergiades, T. & Bassiliades, N. 2013. Ontology-based sentiment analysis of twitter posts. Expert Systems with Applications 40(10): 4065–4074.

Liu, B. 2012. Sentiment Analysis and Opinion Mining(May): pp. 1–108.

Noy, N.F. & McGuinness, D.L. 2001. What is an ontology and why we need it. http://www.ksl.stanford.edu/people/dlm/papers/ontology-tutorial-noy-mcguinness-abstract.html [18 June 2018]

Obitko, M., Snášel, V. & Smid, J. 2004. Ontology Design With Formal Concept Analysis. Proceedings of the International Workshop on Concept Lattices and their Applications (CLA 2004): pp. 111–119.

Pang, B., Lee, L. & Vaithyanathan, S. 2002. Thumbs up?: sentiment classification using machine learning techniques. Proceedings of the Conference on Empirical Methods in Natural Language Processing: pp. 79–86.

Preethi, P.G., Uma, V. & Kumar, A. 2015. Temporal sentiment analysis and causal rules extraction from tweets for event prediction. Procedia Computer Science 48(C): pp. 84–89.

Priya, M. & Kumar, C.A. 2015. A survey of state of the art of ontology construction and merging using Formal Concept Analysis. Indian Journal of Science and Technology 8(24): pp. 1-7.

Sam, K.M. 2013. Ontology-Based Sentiment Analysis Model of Customer Reviews for Electronic Products. International Journal of e-Education, e-Business, e-Management and e-Learning 3(6): pp. 477-482.

Saranya, K & Jayanthy. S. 2017. Learning Techniques. International Conference of Innovations in information Embedded and Communication System: pp. 1–18.

Shein, K.P.P. & Nyunt, T.T.S. 2010. Sentiment Classification Based on Ontology and SVM Classifier. 2010 Second International Conference on Communication Software and Networks: pp. 169–172.

Tavish Srivastava. 2014. Introduction to KNN, K-Nearest Neighbors : Simplified. https://www.analyticsvidhya.com/blog/2014/10/introduction-k-neighbours-algorithm-clustering/ [25 June 2018]

Vijayarani, S., Ilamathi, J. & Nithya, M. 2015. Preprocessing Techniques for Text Mining - An Overview. International Journal of Computer Science & Communication Networks 5(1): pp. 7–16.

Yaakub, M.R., Li, Y., Algarni, A. & Peng, B. 2012. Integration of opinion into customer analysis model. Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2012: pp. 164–168.


Refbacks

  • There are currently no refbacks.


e-ISSN : 2289-2192

For any inquiry regarding our journal please contact our editorial board by email apjitm@ukm.edu.my