Opinion Mining Framework in the Education Domain

Authors: A. M. H. Elyasir, K. S. M. Anbananthen

Abstract:

The internet is growing larger and becoming the most popular platform for the people to share their opinion in different interests. We choose the education domain specifically comparing some Malaysian universities against each other. This comparison produces benchmark based on different criteria shared by the online users in various online resources including Twitter, Facebook and web pages. The comparison is accomplished using opinion mining framework to extract, process the unstructured text and classify the result to positive, negative or neutral (polarity). Hence, we divide our framework to three main stages; opinion collection (extraction), unstructured text processing and polarity classification. The extraction stage includes web crawling, HTML parsing, Sentence segmentation for punctuation classification, Part of Speech (POS) tagging, the second stage processes the unstructured text with stemming and stop words removal and finally prepare the raw text for classification using Named Entity Recognition (NER). Last phase is to classify the polarity and present overall result for the comparison among the Malaysian universities. The final result is useful for those who are interested to study in Malaysia, in which our final output declares clear winners based on the public opinions all over the web.

Keywords: Entity Recognition, Education Domain, Opinion Mining, Unstructured Text.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336262

References:

[1] N. Archak, A. Ghose, and P.G Ipeirotis, "Deriving the Pricing Power of Product Features by Mining Consumer Reviews." Management Science, Vol 57(8), 2011, pp. 1485–1509.
[2] S. Baccianella, A. Esuli, and F. Sebastiani, "SENTIWORDNET 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining." Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), 2010.
[3] S. Park, M. Ko, J. Kim, Y. Liu, and J. Song, "The Politics of Comments: Predicting Political Orientation." Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW 2011), 2011, pp.113-122.
[4] M. Thelwall, D. Wilkinson, and S. Uppal. "Data Mining Emotion in Social Network Communication: Gender differences in MySpace." Journal of the American Society for Information Science and Technology, Vol 61, 2010: 190–199.