Learned ontology guided opinions analysis of extracted aspects from online product reviews

Learned ontology guided opinions analysis of extracted aspects from online product reviews

Ravi kumar K.C. Teja Santosh Dandibhotla Vishnu Vardhan Bulusu  

JNTUH, Telangana State, India

Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar,Telangana State, India

JNTUHCEM, Manthani, Telangana State, India

Corresponding Author Email: 
kcravikumar1971@gmail.com
Page: 
https://jesa.revuesonline.com/accueil.jsp
|
DOI: 
https://doi.org/10.3166/JESA.51.25-49
Received: 
|
Accepted: 
|
Published: 
June 2018
| Citation

OPEN ACCESS

Abstract: 

The opinions expressed by the consumers on online product reviews in e-commerce websites play major role in judging the evaluative character of the product aspect. These expressed opinions lack conceptual preciseness allowing consumers to use them in both syntactically and semantically different ways (lexical variations) on various aspects in the reviews. Also some section of consumers present their opinions in the implicit manner. The evaluation of these types of opinions for opinion orientations raises the semantic gap between the human language and the actual opinionated knowledge. Thus, extracting all these types of opinions on the product aspects may bridge the semantic gap and thereby improving the accuracy of the opinion orientation. In this paper, iterative ontology learning approach is carried out in order to solve the aforementioned problems. In the proposed method, first the pre-processed product reviews are analyzed for extracting opinionated lexical variations. Then, the reviews are further analyzed to extract the implicit opinions. Further, these opinionated lexical variations and implicit opinions with the reviews are formalized for ontology learning. The aspect, opinion pair is formed by reasoning the learned ontology.   Finally, the aspect’s opinion orientation is ascertained by using the sentiwordnet scores in the improved geodesic distance metric. The evaluation of semantic orientation of opinions using the learned ontology guidance against the state-of-the-art approaches shows the effectiveness of the proposed method.

Keywords: 

online reviews, product aspects, opinions, adjective, lexical variations, implicit opinions, ontology learning, semantic orientation

1. Introduction
2. Background and related works
3. Methodology
4. Experimental setup and data analysis
5. Results discussions and evaluation
6. Conclusion and future work
7. Websites list
  References

Benamara F., Cesarano C., Picariello A., Recupero D. R., Subrahmanian V. S. (2007). Sentiment analysis: Adjectives and adverbs are better than adjectives alone. International Conference on Weblogs and Social Media, pp. 1-7.

Buitelaar P., Cimiano P. (2008). Ontology learning and population: bridging the gap between text and knowledge. Amsterdam: IOS, Press, pp. 45-69. https://doi.org/10.3233/978-1-60750-072-8-187

Buitelaar P., Cimiano P., Magnini B. (2006). Ontology learning from text: methods, evaluation and applications. Computational Linguistics, Vol. 32, No. 4, pp. 569-572. https://doi.org/10.1162/coli.2006.32.4.569

De Marneffe M. C., Manning C. D. (2008). Stanford typed dependencies manual. Stanford University, pp. 338-345.

Fellbaum C. (1998). WordNet: An Electronic Lexical Database. Cambridge. MA: MIT Press.

Grondelaers S., Speelman D., Geeraerts D. (2012). Lexical variation and change. The Oxford Handbook of Cognitive Linguistics, pp. 988-1011. https://doi.org/10.1017/CBO9780511841613.021

Hatzivassiloglou V., McKeown K. R. (1997). Predicting the semantic orientation of adjective. Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics, pp. 174-181. https://doi.org/10.3115/976909.979640

Hu M., Liu B. (2004). Mining and summarizing customer reviews. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168-177. https://doi.org/10.1145/1014052.1014073

Huang H. H., Wang J. J., Chen H. H. (2017). Implicit opinion analysis: Extraction and polarity labelling. Journal of the Association for Information Science and Technology, Vol. 68, No. 9, pp. 2076-2087. https://doi.org/10.1002/asi.23835

Johnson M. (2002). Squibs and discussions: the DOP Estimation method is biased and inconsistent. Computational Linguistics, Vol. 28, No. 1, pp. 71-76. https://doi.org/10.1162/089120102317341783

Kamal A., Abulaish M. (2016). OntoLSA—An integrated text mining system for ontology learning and sentiment analysis. Sentiment Analysis and Ontology Engineering, pp. 399-423. https://doi.org/10.1007/978-3-319-30319-2_16

Kamps J., Marx M., Mokken R. J., De Rijke M. (2004). Using WordNet to Measure Semantic Orientations of Adjectives. Language Resources and Evaluation Conference, Vol. 4, pp. 1115-1118. https://doi.org/10.1.1.134.483

Lau R. Y., Lai C. C., Ma J., Li Y. (2009). Automatic domain ontology extraction for context-sensitive opinion mining. International Conference on Information Systems, pp. 35-53.

Liu B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, Vol. 5, No. 1, pp. 1-167. https://doi.org/10.2200/S00416ED1V01Y201204HLT016.

Montemagni S., Wieling M. (2016). Tracking linguistic features underlying lexical variation patterns: A case study on Tuscan dialects. The Future of Dialects: Selected Papers from Methods in Dialectology XV, pp. 117-134. https://doi.org/10.17169/langsci.b81.146

Morris C. (1958). The measurement of meaning. Charles E. Osgood, George J. Suci , Percy H. Tannenbaum. American Journal of Sociology, Vol. 63, No. 5, pp. 550-551. https://doi.org/10.1086/222316

Navigli R., Velardi P. (2010). Learning word-class lattices for definition and hypernym extraction. Annual Meeting of the Association for Computational Linguistics, pp. 1318-1327. https://doi.org/10.1.1.172.7430

Pang B., Lee L., Vaithyanathan S. (2002). Thumbs up?: sentiment classification using machine learning techniques. ACL-02 Conference on Empirical Methods in Natural Language Processing, Vol. 10, pp. 79-86. https://doi.org/10.3115/1118693.1118704

Polpinij J., Ghose A. K. (2008). An ontology-based sentiment classification methodology for online consumer reviews. International Conference on Web Intelligence and Intelligent Agent Technology, IEEE Computer Society, Vol. 01, pp. 518-524. https://doi.org/10.1109/WIIAT.2008.68

Qiu G., Liu B., Bu J., Chen C. (2011). Opinion word expansion and target extraction through double propagation. Computational Linguistics, Vol. 37, No. 1, pp. 9-27. https://doi.org/10.1162/coli_a_00034

Rada R., Mili H., Bicknell E., Blettner M. (1989). Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 19, No. 1, pp. 17-30. https://doi.org/10.1109/21.24528

Ravi K., Ravi V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-based Systems, Vol. 89, pp. 14-46. https://doi.org/10.1016/j.knosys.2015.06.015

Ravi Kumar K. C., Teja Santosh D., Vishnu Vardhan B. (2017). Determining the Semantic Orientation of opinion words using typed dependencies for opinion word senses and Sentiwordnet scores from online product reviews. International Journal of Knowledge and Web Intelligence. https://doi.org/10.1504/IJKWI.2017.10010171

Santosh D. T., Vardhan B. V. (2016). PROO ontology development for learning feature specific sentiment relationship rules on reviews categorisation: A semantic data mining approach. International Journal of Metadata, Semantics and Ontologies, Vol. 11, No. 1, pp. 29-38. https://doi.org/10.1504/IJMSO.2016.078105

Speer R., Havasi C. (2012). Representing general relational knowledge in ConceptNet 5. Language Resources and Evaluation Conference, pp. 3679-3686. https://doi.org/10.1.1.383.5758

Sun C., Zuo Z. S., Lu W., Liu X. T., Guo X. L., Liu F. (2017). Visualization of the heat transfer character of dry slag discharge system. International Journal of Heat and Technology, Vol. 35, No. 4, pp. 793-798. https://doi.org/10.18280/ijht.350414

Tho Q. T., Hui S. C., Fong A. C. M., Cao T. H. (2006). Automatic fuzzy ontology generation for semantic web. IEEE Transactions on Knowledge and Data Engineering, Vol. 18, No. 6, pp. 842-856. https://doi.org/10.1109/TKDE.2006.87

Toutanova K., Manning C. D. (2000). Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. Annual Meeting of the Association for Computational Linguistics, Vol. 13, pp. 63-70. https://doi.org/10.3115/1117794.1117802

Turney P. D., Littman M. L. (2003). Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS), Vol. 21, No. 4, pp. 315-346. https://doi.org/10.1145/944012.944013

van Miltenburg E. (2016). WordNet-based similarity metrics for adjectives. Global WordNet Conference, Global WordNet Association, pp. 414-418.

Volkova S., Wilson T., Yarowsky D. (2013). Exploring demographic language variations to improve multilingual sentiment analysis in social media. Conference on Empirical Methods in Natural Language Processing, pp. 1815-1827.

Wang T., Hirst G. (2012). Exploring patterns in dictionary definitions for synonym extraction. Natural Language Engineering, Vol. 18, No. 3, pp. 313-342. https://doi.org/10.1017/S1351324911000210