Every system needs continuous improvement. Feedback from different stakeholders plays a crucial role here. From literature study, the need of textual feedback analysis for an academic institute is well established. In fact, it has been perceived that often a textual feedback is more informative, more open ended and more effective in producing actionable insights to decision makers as compared to more common score based (on a scale from 1: n) feedback. However, getting this information from textual feedback is not possible through the traditional means of data analysis. Here we have conceptualized a tool, which can apply text mining techniques to elicit insights from textual data and has been published as an open source package for a broader use by practitioners. Appropriate visualization techniques are applied for intuitive understanding of the insights. For this, we have used a real dataset consisting of alumni feedback from a top engineering college in Kolkata.
CITATION STYLE
Bose, S., Saha, U., Kar, D., Goswami, S., Nayak, A. K., & Chakrabarti, S. (2017). Rsentiment: A tool to extract meaningful insights from textual reviews. In Advances in Intelligent Systems and Computing (Vol. 516, pp. 259–268). Springer Verlag. https://doi.org/10.1007/978-981-10-3156-4_26
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