Evaluating and Summarizing Student’s Feedback Using Opinion Mining
Keywords:
Opining Mining, Information Retrieval, Text Summarization, Text MiningAbstract
Collecting Students feedbacks for the subjects taught is a regular activity of an academic institution. Automating the process of collecting these feedbacks becomes an important requirement. This provides an opportunity to analyze these feedbacks efficiently and summarize the performance of a teacher in the subjects he taught. Opinion mining (or Sentiment Analysis) which is generally used for classifying customer reviews in terms of positive or negative sentiments can be used effectively in evaluating and summarizing the student’s feedback.
References
K. Dave, S. Lawrence, and D. M. Pennock, “Mining the peanut gallery: Opinion extraction and semantic classification of product reviews,” in Proceedings of WWW, pp. 519–528, 2003.
B. Pang and L. Lee, “A Sentimental Education: Sentiment Analysis Using Subjectivity”, Proc. of ACL, pp. 271-278, 2004.
M. Hu and B. Liu, Mining and summarizing customer reviews. In Proc. of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD `04). ACM, USA, pp. 168-177, 2004.
K. P. Shein and T. S. Nyunt, "Sentiment Classification Based on Ontology and SVM Classifier," Communication Software and Networks, 2010. ICCSN `10. Second International Conference on, Singapore, pp. 169-172, 2010.
T. Mullen and N. Collier, “Sentiment analysis using support vector machines with diverse information Sources,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 412–418, July, 2004.
K. Lerman, S. Goldensohn, and R. McDonald. Sentiment summarization: evaluating and learning user preferences. In Proc. of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL `09). Association for Computational Linguistics, USA, pp. 514-522, 2009.
T. Joachims, Learning to Classify Text Using Support Vector Machines, Kluwer Academic Publishers, 2001.
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