RSentiment: A Tool to Extract Meaningful Insights from Textual Reviews

  • Subhasree Bose
  • Urmi Saha
  • Debanjana Kar
  • Saptarsi Goswami
  • Amlan Kusum Nayak
  • Satyajit Chakrabarti
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 516)


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.


Textual feedback Sentiment analysis Topic models 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Subhasree Bose
    • 1
  • Urmi Saha
    • 1
  • Debanjana Kar
    • 1
  • Saptarsi Goswami
    • 1
  • Amlan Kusum Nayak
    • 1
  • Satyajit Chakrabarti
    • 1
  1. 1.Institute of Engineering and ManagementKolkataIndia

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