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 CN  Vol.8 No.3 , August 2016
Hybrid Algorithm to Evaluate E-Business Website Comments
Abstract: Online reviews are considered of an important indicator for users to decide on the activity they wish to do, whether it is watching a movie, going to a restaurant, or buying a product. It also serves businesses as it keeps tracking user feedback. The sheer volume of online reviews makes it difficult for a human to process and extract all significant information to make purchasing choices. As a result, there has been a trend toward systems that can automatically summarize opinions from a set of reviews. In this paper, we present a hybrid algorithm that combines an auto-summarization algorithm with a sentiment analysis (SA) algorithm, to offer a personalized user experiences and to solve the semantic-pragmatic gap. The algorithm consists of six steps that start with the original text document and generate a summary of that text by choosing the N most relevant sentences in the text. The tagged texts are then processed and then passed to a Naive Bayesian classifier along with their tags as training data. The raw data used in this paper belong to the tagged corpus positive and negative processed movie reviews introduced in [1]. The measures that are used to gauge the performance of the SA and classification algorithm for all test cases consist of accuracy, recall, and precision. We describe in details both the aspect of extraction and sentiment detection modules of our system.
Cite this paper: M. Rababah, O. , K. Hwaitat, A. , Al Qudah, D. and Halaseh, R. (2016) Hybrid Algorithm to Evaluate E-Business Website Comments. Communications and Network, 8, 137-143. doi: 10.4236/cn.2016.83014.
References

[1]   Pang, B. and Lee, L. (2004) A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics.
http://dx.doi.org/10.3115/1218955.1218990

[2]   D’Andrea, A., Ferri, F., Grifoni, P. and Guzzo. T. (2015) Approaches, Tools and Applications for Sentiment Analysis Implementation. International Journal of Computer Applications, 125, 26-33.

[3]   Abdulla, N., Ahmed, N., Shehab, M., AlAyyoub, M., Al-Kabi, M. and Al-Rifai, S. (2014) Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis. International Journal of Information Technology and Web Engineering (IJITWE), 9, 55-71.
http://dx.doi.org/10.4018/ijitwe.2014070104

[4]   Sindhu, R., Jamail, R. and Kumar, R. (2014) A Novel Approach for Sentiment Analysis and Opinion Mining. International Journal of Emerging Technology and Advanced Engineering, 4, 522-527.

[5]   Nasukawa, T. and Yi, J. (2003) Sentiment Analysis: Capturing Favorability Using Natural Language Processing. Proceedings of the 2nd International Conference on Knowledge Capture, Florida, 23-25 October 2003, 70-77.
http://dx.doi.org/10.1145/945645.945658

[6]   Morinaga, S., Yamanishi, K., Tateishi, K. and Fukushima, T. (2002) Mining Product Reputations on the Web. Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 341-349.
http://dx.doi.org/10.1145/775047.775098

[7]   Pang, B., Lee, L. and Vaithyanathan, S. (2002) Thumbs up? Sentiment Classification Using Machine Learning Techniques. Proceedings of the 7th Conference on Empirical Methods in Natural Language Processing, 79-86.

[8]   Tong, R.M. (2001) An Operational System for Detecting and Tracking Opinions in On-Line Discussion. Proceedings of SIGIR Workshop on Operational Text Classification.

[9]   Turney, P. (2002) Thumbs up or Thumbs down? Semantic Orientation Applied to Unsupervised Classification of Reviews. Proceedings of the 40th ACL, 417-424.

[10]   Wiebe, J. (2000) Learning Subjective Adjectives from Corpora. Proceedings of National Conference on Artificial Intelligence.

[11]   Wilson, T., Wiebe, J. and Hoffmann, P. (2009) Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis. Computational Linguistics, 35, 399-433.
http://dx.doi.org/10.1162/coli.08-012-R1-06-90

[12]   Hatzivassiloglou, V. and McKeown, K.R. (1997) Predicting the Semantic Orientation of Adjectives. Proceedings of the 8th Conference on European Chapter of the Association for Computational Linguistics Madrid, Spain, 174-181.

[13]   Yi, J., Nasukawa, T., Niblack, W. and Bunescu, R. (2003) Sentiment Analyzer: Extracting Sentiments about a Given Topic Using Natural Language Processing Techniques. Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), Florida, 19-22 November 2003, 427-434.
http://dx.doi.org/10.1109/icdm.2003.1250949

[14]   Hiroshi, K., Tetsuya, N. and Hideo, W. (2004) Deeper Sentiment Analysis Using Machine Translation Technology. Proceedings of the 20th International Conference on Computational Linguistics (COWLING 2004), Geneva, 23-27 August 2004, 494-500.
http://dx.doi.org/10.3115/1220355.1220426

[15]   Govindarajan, M. (2013) Sentiment Analysis of Movie Reviews Using Hybrid Method of Naive Bayes and Genetic Algorithm. International Journal of Advanced Computer Research, 3, 139-145.

[16]   Albanese, M. (2013) A Multimedia Recommender System. ACM Transactions on the Internet Technology (TOIT), 13, 1-32.
http://dx.doi.org/10.1145/2532640

[17]   Branavan, S., Chen, H., Eisenstein, J. and Barzilay, R. (2008) Learning Document-Level Semantic Properties from Free-Text Annotations. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 263-271.

[18]   Carenini, G. and Cheung, J. (2008) Extractive vs. NLG-Based Abstractive Summarization of Evaluative Text: The Effect of Corpus Controversiality. Proceedings of the International Natural Language Generation Conference, 33-41.
http://dx.doi.org/10.3115/1708322.1708330

[19]   Lerman, K., Blair-Goldensohn, S. and McDonald, R. (2009) Sentiment Summarization: Evaluating and Learning User Preferences. Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics, 514-522.
http://dx.doi.org/10.3115/1609067.1609124

[20]   NLTK.org. (n.d.). Retrieved 01 28, 2016.
http://www.nltk.org/index.html

[21]   Powers, D. (2011) Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation. Journal of Machine Learning Technologies, 2, 37-63.

[22]   Gaines, P. (2016) Accuracy, Precision, Mean and Standard Deviation. ICP Operations Guide Part 14, On 25 May.
http://www.inorganicventures.com/accuracy-precision-mean-and-standard-deviation

 
 
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