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首页> 外文会议>International Conference on Frontiers of Intelligent Computing : Theory and Applications >RSentiment: A Tool to Extract Meaningful Insights from Textual Reviews
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RSentiment: A Tool to Extract Meaningful Insights from Textual Reviews

机译：Rsentiment：一种从文本评论中提取有意义的见解的工具

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摘要

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.
机译：每个系统都需要持续改进。 来自不同利益相关者的反馈在这里发挥着至关重要的作用。 从文学研究中，学术研究所的文本反馈分析的需要很好。 事实上，它已经被认为是文本反馈通常是更有信息丰富的，更开放的结局，更有效地为决策者产生了相比，与基于更常见的分数（从1：n）反馈的反馈相比，更有效。 但是，通过传统的数据分析方法无法从文本反馈中获取此信息。 在这里，我们已经概念化了一种工具，可以将文本挖掘技术应用于从文本数据中引出洞察力，并已作为从业者更广泛使用的开源包。 适当的可视化技术用于直观地了解见解。 为此，我们使用了由Kolkata顶级工程学院的校友反馈组成的真实数据集。

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