No abstract

No abstract

No abstract

PurposeThe purpose of this paper is to investigate the sales impact of different types of online word-of-mouth based on their source (user vs critic) and form (structured vs unstructured).Design/methodology/approachThe paper proposed a model by adopting the heuristic-systematic perspective of information processing and tested it using online movie reviews collected from Rotten Tomatoes. A unique dataset was constructed, which matched critic reviews and user reviews with metadata such as box-office sales and advertisement spending for 90 movies. Sentiment information from the textual contents of both user and critic reviews were text-mined and extracted. Data analyses were used to compare the box-office responsiveness of four types of reviews: user numeric ratings, user text reviews, critic numeric ratings and critic text reviews.FindingsCritic reviews and user reviews influence sales through different forms: while user reviews impact sales through their aggregate numeric ratings, critic reviews exert their impact through textual narratives.Practical implicationsThis study provides managerial implications to businesses on how to allocate their resources on different social media-related marketing strategies to maximize the economic value of online user-generated information.Originality/valueThe major contribution of this study is to extend the current understanding of the sales impact of online reviews to their textual aspect, as well as investigate how these textual narratives play different roles when offered by critics and users.

We demonstrate the use of sequence pattern mining as applied to monitoring the usage of emailing software by clients with cognitive impairments. We show how Max Motif, a sequence-mining algorithm, can be applied using a stream-mining approach. Consequently, clinicians can now consider sequential patterns in their analysis of client emailing behaviors. Such analysis is part of the Think and Link project, which provides personalized email clients, a kind of assistive technology, to aid client communications that facilitate activities of daily living. By using the simplified, customized system, clients can now email, whereas they could not previously. By continuously monitoring usage, the project is able continually adapt the software to a user's changing needs. Thus, monitoring software usage, particularly email event sequences, is important. This paper introduces theory and design of stream sequence-mining for UI event streams.

No abstract

Users with cognitive impairments use assistive technology (AT) as part of a clinical treatment plan. As the AT interface is manipulated, data stream mining techniques are used to monitor user goals. In this context, realtime data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment, as the user learns his or her personalized emailing system. When the quality of some data-mined models varies significantly from nearby models-as defined by quality metrics-the user's behavior is then flagged as a significant behavioral change. The specific changes in user behavior are then characterized by differencing the data-mined decision tree models. This article describes how model quality monitoring and decision tree differencing can aid in recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The technique may be more widely applicable to other real-time data-intensive analysis problems.

Users with cognitive impairments use assistive technology as part of a treatment plan. As the AT interface is manipulated, data stream mining techniques are used to monitor user goals. In this context, data mining aids caregivers in tracking user behaviors as they attempt to achieve their goals. Divergences over consecutive stream-mined models identify potential changes in user goal attainment, as the user learns his or her personalized emailing system. When a data-mined model diverges significantly from recent models, the user's behavior is flagged as a significant behavioral change. The specific changes in behavior are then characterized by analyzing model divergence as well as the underlying data. This chapter describes how divergence analysis of decision-tree and hidden Markov models can aid recognition and diagnoses of behavioral changes in support of AT adaptation, in a case study of cognitive AT for emailing. The technique may be more widely applicable to other behavior monitoring contexts.