Combining different evaluation systems on social media for measuring user satisfaction. Issue 4 (July 2018)

• Record Type: Journal Article
• Authors: Balbi, Simona; Misuraca, Michelangelo; Scepi, Germana
• Is Part Of: Information processing & management. Volume 54:Issue 4(2018:Jul.)
• Page Start: 674
• Page End: 685
• Publication Date: 2018-07
• Subjects: Social media -- Sentiment analysis -- Rating -- Knowledge management; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2018.04.009
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: User satisfaction is often evaluated on social media both with ratings and written reviews. A strategy for combining the different sources of available data is here proposed. Incorporating review polarity in the rating improved the evaluation, moving from an ordinal scale to a continuous scale. The new measure has a finer granularity and is more informative, also enabling the reviews to be ranked. Abstract: 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.