Automated Evaluation of Students’ Feedbacks using Text Mining Methods
Sartaj Ahmad1, Ashutosh Gupta2, Neeraj Kumar Gupta3
1Sartaj Ahmad*, IT Department, KIET Group of Institutions affiliated to AKTU Lucknow, Ghaziabad, India.
2Ashutosh Gupta, School of Science in UPRTU, Allahabad, India.
3Neeraj Kumar Gupta, EN Department, KIET Group of Institution affiliated to AKTU Lucknow, Ghaziabad, India.

Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 337-342 | Volume-8 Issue-4, November 2019. | Retrieval Number: D6846118419/2019©BEIESP | DOI: 10.35940/ijrte.D6846.118419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: In this era of competition there is a culture of online reviews or feedbacks. These feedbacks may be about any product or service. However, major issues are their unstructured textual form and big number. It means every user gives feedback in own style. Study and analyzing of such unorganized big number of feedbacks that are growing every year becomes herculean task. This paper describes about mining of structured data (table) and unstructured data (text) both. An application from academic environment for structured and unstructured form of data is considered and discussed to enhance understanding and easiness of researcher. Stanford Parser plays a very useful role to understand the semantic of a sentence. It gives a base that how to separate data from the wellspring of information accessible in the literary structure like web based life, tweets, news, books and so on. It is also helpful to judge a teaching learning process in terms of teacher’s performance and subject’s weakness if any. This paper has five sections first about introduction, second about literature of text mining and its techniques, third about proposed work and result, fourth about future perspectives and finally fifth as a conclusion.
Keywords: Content Mining, Data Mining, Information Retrieval, Knowledge Extraction, Text Mining, Unstructured Data Mining.
Scope of the Article: Data Mining.