Ontology Based Opinion Mining for Book Reviews

Firzhan NaqashSenior Software Engineer

Outline

● �Introduction● �Contents of Book Review● �Methodology● �Feature Identification● �Polarity Identification● �Sentiment Analysis● �Conclusion● �References●

Introduction

● �The recent burst in the web usage has contributed to the growth of number of various online reviews.

● �Most of the reviews are objective where as some are context sensitive and subjective.

● �All these reviews contain mixture of negative, positive or neutral comments.

● �In today’s world people tend to go through these online reviews before going ahead with any activities like shopping, purchasing, and reading a book etc …

● �This research aims on assisting the people by developing an ontology for various online book reviews.

Introduction ...

● ��Ontology is a formal and explicit domain specific

reference model.● �Ontology reference model can be used for defining

set of concepts along with the relationship among them.

● Therefore this nature provides an efficient way of performing opinion mining on book reviews.

Introduction ...

● �General opinion mining's are more focused on

1. Context-free sentiment classification 2. Large number of manually annotated training examples.● This project focuses on context-sensitive opinion

mining system.

Contents of Book Review�

● Description.● �Narration.● �Exposition.● �Argument.● �State of Knowledge.● �Content Description.● �Subject Area.

Methodology

Ontology Development�

● �Ontology development is based on the domain ( Domain Ontology ).

● �Task of ontology construction is divided in to two.

1. Select the relevant sentences including conceptions. 2. Extract the conceptions from those sentences.

Ontology Development ...�

● ��Two models are being created. Those are➢ Feature Model➢ Book Model

Ontology Development ...�

● ��Two models are being created. Those are➢ Feature Model➢ Book Model

Ontology Development …..

Feature Identification�

● �This process is used for feature identification of ontology terminologies.

● Extraction of the related sentence which contain ontology terminologies.

● Those sentences can be used for feature extraction.

Feature Identification ...�

Polarity Identification�

● �Initially two well known approaches are considered.❖ SentiWordNet❖ WordNet-Affect

● �Eventually we decided to use WordNet-Affect.

WordNet-Affect …�

● ���Emotional causes can be calculated in two different ways.❖ Direct Affective Words.❖ Indirect Affective Words.

● Affective weight is calculated based on semantic similarity mechanism which acquires from large corpus of texts.

● �Semantic Affinity for each emotion is returned.

● �Eventually we decided to use WordNet-Affect.

Polarity Identification�

Polarity Identification ...�

Sentiment Analysis�

● ����Using the final lists of positive, negative and neutral words or phrases, opinion orientation expressed on each feature can be analyzed.

● �Hierarchy structure is used to calculate the opinion of high level concept.

Sentiment Analysis ...��

Conclusion�

● �����This research attempts to create an ontology for the book domain to perform opinion mining.

● During the opinion mining process, polarity of the word is being identified by using WordNet-Affect.

Q&A

References�● Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?

sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2002 (2002)

● Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2005 (2005)

● Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of ACM SIGKDD conference, KDD 2004 (2004)

References ...● �Kaji, N., Kitsuregawa, M.: Automatic construction of

polarity-tagged corpus from html documents. In: Proceedings of the COLING/ACL on Main conference poster sessions, Association for Computational Linguistics Morristown, NJ, USA, pp. 452–459 (2006).

● Hu, M., Liu, B.: Mining opinion features in customer reviews. In: Proceedings of AAAI, pp. 755–760 (2004).

● �Carenini, G., Ng, R., Pauls, A.: Interactive multimedia summaries of evaluative text. In: Proceedings of the 11th international conference on Intelligent user interfaces,pp. 124–131. ACM, New York (2006).

References ...● Ding, X., Liu, B.: The utility of linguistic rules in opinion

mining. In: Proceedings of SIGIR 2007 (2007)● Gruber, T.R.: A translation approach to portable ontology

specifications. Knowledge Acquisition 5, 199–220 (1993)● �Pang, B.: Seeing stars: Exploiting class relationships for

sentiment categorization with respect to rating scales. Ann. Arbor. 100 (2005).

● Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In:Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2003), pp. 105–112 (2003)

References ...● Turney, P., et al.: Thumbs up or thumbs down? semantic

orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp. 417–424 (2002).

● Gamon, M., Aue, A., Corston-Oliver, S., Ringger, E.: Pulse: Mining customer opinions from free text. In: Famili, A.F., Kok, J.N., Pe˜na, J.M., Siebes, A., Feelders,� A. (eds.) IDA 2005. LNCS, vol. 3646, pp. 121–132. Springer, Heidelberg (2005)

● Dave, K., Lawrence, S., Pennock, D.: Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th international conference on World Wide Web, pp. 519–528. ACM, New York (2003)

References ...● Hearst, M.A.: Direction-based text interpretation as an

information access refinement, pp. 257–274 (1992)● Jacquemin, C.: Spotting and Discovering Terms through

Natural Language Processing. MIT Press, Cambridge (2001)

● Kobayashi, N., Inui, K., Matsumoto, Y.: Collecting evaluative express for opinion extraction. In: Proceedings of the International Joint Conference on Natural Language Processing, IJCNLP (2004)

● �Yi, J., Bunescu, T.N., Niblack, R.W.: Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In: Proceedings of IEEE International Conference on Data Mining, ICDM 2003 (2003)

References ...● Hatzivassiloglou, V., McKeown, K.: Predicting the semantic

orientation of adjectives. In: Proceedings of ACL-EACL 1997 (1997)

● Kanayama, H., Nasukawa, T.: Fully automatic lexicon expansion for domainoriented sentiment analysis. In: Proceedings of the Conference on Empirical Methods �in Natural Language Processing, EMNLP 2006 (2006)

● Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of 5th Conference on Language Resources and Evaluation, LREC 2006 (2006).

● Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of 5th Conference on Language Resources and Evaluation, LREC 2006 (2006).

References ...● Book Review Guidelines. Available at [http://www.write.

armstrong.edu/handouts/BookReview.pdf]. Accessed on 14/12/2013.

● �Samaneh Moghaddam & Martin Ester : Mining in Online Reviews: Recent Trends. Simon Fraser University Tutorial.

● Efstratios Kontopoulos, Christos Berberidis, Theologos Dergiades, Nick Bassiliades : Ontology-based Sentiment Analysis of Twitter Posts , Expert Systems with Applications (2011).

● Natalya F. Noy and Deborah L. McGuinness: Ontology Development 101: A Guide to Creating Your First Ontology, Stanford University, Stanford, CA, 94305 ( 2009 )

References ...● A. Valitutti, C. Strapparava, and O. Stock. Developing

affective lexical resources. Psychnology: 2 (1), 2004