RSentiment: A Tool to Extract Meaningful Insights from Textual Reviews
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 ofte
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Abstract 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. Keywords Textual feedback
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Sentiment analysis
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Topic models
S. Bose ⋅ U. Saha (✉) ⋅ D. Kar ⋅ S. Goswami ⋅ A.K. Nayak ⋅ S. Chakrabarti Institute of Engineering and Management, Kolkata, India e-mail: [email protected] S. Bose e-mail: [email protected] D. Kar e-mail: [email protected] S. Goswami e-mail: [email protected] A.K. Nayak e-mail: [email protected] S. Chakrabarti e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2017 S.C. Satapathy et al. (eds.), Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications, Advances in Intelligent Systems and Computing 516, DOI 10.1007/978-981-10-3156-4_26
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1 Introduction Growth of textual data has been on the rise for last few years. Some examples of textual data are reviews or feedback, emails, chat or transcripts, tweets, blogs etc. Feedback and reviews have been effectively used for many significant insights. It is critical for any dynamic system or process. The feedback can be quantitative where users are asked to rate on a given scale of (say) 1 to 5. A specific question can be asked and the participant will be asked to choose options like Strongly Agree, Somewhat Agree, Neither Agree nor Disagree, Somewhat disagree, Strongly Disagree. On the other hand, it can be qualitative where feedback about a particular area or areas of interest may be asked for. A sentiment analysis strategy on the qualitative data can also give enough actionable intelligence. The quantitative feedback can alternatively be thought as the structured data, whereas the qualitative feedback represents unstructured data. Undoubtedly having quantitative feedback has its own advantages. In paper [1], some of the shortcomings pointed out by authors on quantitative feedback are (i) Good survey questions are hard to write (ii) data may provide a generic picture but lacks depth. Strictly, as far as an academic sector is concerned, from our own experience it was felt that, a question on infrastructur
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