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Combining different evaluation systems on social media for measuring user satisfaction

机译：结合社交媒体上的不同评估系统以衡量用户满意度

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摘要

Web 2.0 allows people to express and share their opinions about products and services they buy/use. These opinions can be expressed in various ways: numbers, texts, emoticons, pictures, videos, audios, and so on. There has been great interest in the strategies for extracting, organising and analysing this kind of information. In a social media mining framework, in particular, the use of textual data has been explored in depth and still represents a challenge. On a rating and review website, user satisfaction can be detected both from a rating scale and from the written text. However, in common practice, there is a lack of algorithms able to combine judgments provided with both comments and scores. In this paper we propose a strategy to jointly measure the user evaluations obtained from the two systems. Text polarity is detected with a sentiment-based approach, and then combined with the associated rating score. The new rating scale has a finer granularity. Moreover, also enables the reviews to be ranked. We show the effectiveness of our proposal by analysing a set of reviews about the Uffizi Gallery in Florence (Italy) published on TripAdvisor.
机译：Web 2.0允许人们表达和分享他们对所购买/使用的产品和服务的看法。这些意见可以通过多种方式表达：数字，文本，表情符号，图片，视频，音频等。人们对提取，组织和分析此类信息的策略非常感兴趣。特别是在社交媒体挖掘框架中，已经深入探索了文本数据的使用，但仍然是一个挑战。在评级和评论网站上，可以从评级量表和书面文字中检测用户满意度。但是，在通常的实践中，缺少能够将注释和分数一起提供的判断相结合的算法。在本文中，我们提出了一种策略来共同衡量从这两个系统获得的用户评价。使用基于情感的方法检测文本极性，然后将其与关联的评分结合在一起。新的等级量表具有更精细的粒度。此外，还可以对评论进行排名。我们通过分析在TripAdvisor上发表的关于佛罗伦萨（意大利）乌菲兹美术馆的一组评论，来展示我们提议的有效性。

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