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Simona Balbi Michelangelo Misuraca Germana Scepi 《Information processing & management》2018,54(4):674-685
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. 相似文献
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The collective feedback of the users of an Information Retrieval (IR) system has been shown to provide semantic information
that, while hard to extract using standard IR techniques, can be useful in Web mining tasks. In the last few years, several
approaches have been proposed to process the logs stored by Internet Service Providers (ISP), Intranet proxies or Web search
engines. However, the solutions proposed in the literature only partially represent the information available in the Web logs.
In this paper, we propose to use a richer data structure, which is able to preserve most of the information available in the
Web logs. This data structure consists of three groups of entities: users, documents and queries, which are connected in a
network of relations. Query refinements correspond to separate transitions between the corresponding query nodes in the graph,
while users are linked to the queries they have issued and to the documents they have selected. The classical query/document
transitions, which connect a query to the documents selected by the users’ in the returned result page, are also considered.
The resulting data structure is a complete representation of the collective search activity performed by the users of a search
engine or of an Intranet. The experimental results show that this more powerful representation can be successfully used in
several Web mining tasks like discovering semantically relevant query suggestions and Web page categorization by topic. 相似文献
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Maria Concetta Capizzo Silvana Nuzzo Michelangelo Zarcone 《European Journal of Engineering Education》2006,31(6):717-727
The case study described in this paper investigates the relationship among some pre-instructional knowledge, the learning gain and the final physics performance of computing engineering students in the introductory physics course. The results of the entrance engineering test (EET) have been used as a measurement of reading comprehension, logic and mathematics skills and basic physics knowledge of a sample of 47 Computing Engineering freshmen at the University of Palermo (Italy). These data give a significant picture of the initial knowledge status of a student choosing engineering studies. The students' physics learning gain has been calculated using a standardized tool in mechanics: the force concept inventory (FCI). The analysis shows that mathematical and physical background contribute to achieve a good final preparation in physics courses of engineering faculties; however the students' learning gain in physics is independent of students' initial level of mathematics skills and physics knowledge. Initial logic skills and reading comprehension abilities are not significant factors for the learning physics gain and the performance on physics courses. 相似文献
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