The sentiment is the action or point of view of a person based on experience. Evaluation for Offices is one way of getting emotions or feedbacks from clients. A person's response serves as an assessment of the quality of higher education standards. For the administrators to gather feedback from the clients/customers regarding their satisfaction or performance inside the offices or department premise, eSurMent was used as a gathering data tool. The study identifies the strengths and weaknesses of Educational Institution Services based on users' positive and negative responses. It provides a sentiment score from the qualitative data and a numerical response rating from the quantitative evaluation data, and a description of the evaluation results from users. Sentiment analysis is one way of surveys and polls to analyze the responses that researchers found to determine positive and negative reactions from students. Therefore, school administrators will be more aware of the shortcomings of users. Reports generated by the system can be used for self-improvement in the institution. In addition, the results of job evaluations can be used as the basis for opportunities, achievements, or marketing strategies.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
 J. Ranjan and K. Malik, “Effective Educational Process: a Data‐mining Approach,” VINE, vol. 37, no. 4, pp. 502–515, 2007.
 D. Kiron, R. Shockley, N. Kruschwitz, G. Finch, and M. Haydock, “Analytics: The widening divide,” MIT Sloan Manag. Rev., vol. 53, no. 2, pp. 3–20, 2012.
 J. Brenan and R. Williams, Collecting and Using Student Feedback. New York, 2004.
 X. Fang and J. Zhan, “Sentiment Analysis Using Product Review Data,” J. Big Data, vol. 2, no. 5, 2015, [Online]. Available: https://doi.org/10.1186/s40537-015-0015-2.
 A. N. M. Zamani, N. Abidin, S.Z.Z., Omar, and M. Z. Z. Abiden, “Sentiment Analysis: Determining People’s Emotions on Facebook. Applied Computational Science,” in Computational Science and Technology, 2013, pp. 111–116.
 A. Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining,” Proc. 7th Int. Conf. Lang. Resour. Eval. Lr. 2010, pp. 1320–1326, 2010, DOI: 10.17148/ijarcce.2016.51274.
 S. Z. Z. Abidin, M. H. M. Omar, N., and M. B. C. Haron, “Quantifying Text-based Public’s Emotion Discussion Issues in Online Forum,” Int. J. New Comput. Archit. their Appl., vol. 1, no. 2, pp. 428–436, 2011.
 J. A. Bargh and K. Y. . McKenna, “The Internet and Social Life,” Annu. Rev. Psychol., vol. 55, pp. 573–590, 2004.
 D. Potena and C. Diamantini, “Mining Opinions on the Basis of Their Affectivity,” Int. Symp. Collab. Technol. Syst., pp. 245–254, 2010.
 S. K. Mohamad and Z. Tasir, “Educational Data Mining: A Review,” Procedia - Soc. Behav. Sci., vol. 97, pp. 320–324, 2013, DOI: 10.1016/j.sbspro.2013.10.240.