RSentiment: A Tool to Extract Meaningful Insights from Textual Reviews
- Cite this paper as:
- 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: Satapathy S., Bhateja V., Udgata S., Pattnaik P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications. Advances in Intelligent Systems and Computing, vol 516. Springer, Singapore
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.