51,255 research outputs found

    Multimodal Content Analysis for Effective Advertisements on YouTube

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    The rapid advances in e-commerce and Web 2.0 technologies have greatly increased the impact of commercial advertisements on the general public. As a key enabling technology, a multitude of recommender systems exists which analyzes user features and browsing patterns to recommend appealing advertisements to users. In this work, we seek to study the characteristics or attributes that characterize an effective advertisement and recommend a useful set of features to aid the designing and production processes of commercial advertisements. We analyze the temporal patterns from multimedia content of advertisement videos including auditory, visual and textual components, and study their individual roles and synergies in the success of an advertisement. The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users. Our proposed framework employs the signal processing technique of cross modality feature learning where data streams from different components are employed to train separate neural network models and are then fused together to learn a shared representation. Subsequently, a neural network model trained on this joint feature embedding representation is utilized as a classifier to predict advertisement effectiveness. We validate our approach using subjective ratings from a dedicated user study, the sentiment strength of online viewer comments, and a viewer opinion metric of the ratio of the Likes and Views received by each advertisement from an online platform.Comment: 11 pages, 5 figures, ICDM 201

    From governance to meta-governance in tourism?: Re-incorporating politics,interests and values in the analysis of tourism governance

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    Despite its theorization in the political and policy sciences in the early 1990s, the concept of metagovernance has gained relatively little recognition in tourism studies. Nevertheless, its significance in the political sciences and policy literature, especially as a result of the perceived failure of governance systems following the recent global financial crisis, has only served to reinforce its relevance. Metagovernance addresses some of the perceived failures of traditional governance approaches and associated interventions, and has enabled the understanding of central-state led regimes of shadowed hierarchical authorities and local-level micro-practices of social innovation and self-government. In contrast, tourism studies have tended to restrict study of the political dimension of tourism governance and the role of the state under the traditional parallelism between government and governance. Examination of how governance is itself governed enables a better understanding of the practices of planning and policy making affecting tourism and destinations. In particular, the applications of concepts of governance are inextricably linked to a given set of value assumptions which predetermine the range of its application. A short example of the application of the metagovernance paradigm is provided from the New Zealand context. It is concluded that governance mechanisms are not value-neutral and instead serve to highlight the allocation of power in a destination and the dominance of particular values and interests

    Basic tasks of sentiment analysis

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    Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about

    eWOM & Referrals in Social Network Services

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    If a few decades ago the development of the Internet was instrumental in the interconnection between markets, nowadays the services provided by Web 2.0, such as social network sites (SNS) are the cutting edge. A proof of this trend is the exponential growth of social network users. The main objective of this work is to explore the mechanisms that promote the transmission and reception (WOM and referrals) of online opinions, in the context of the SNS, by buyers of travel services. The research includes some research lines: technology acceptance model (TAM), Social Identification Theory and Word-of-Mouth communication in virtual environment (eWOM). Based on these theories an explicative model has been proposed applying SEM analysis to a sample of SNS users’ of tourist service buyers. The results support the majority of the hypotheses and some relevant practical and theoretical implications have been pointed out for tourist managers
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