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Abstract Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data—tweet length, spelling errors, abbreviations, and special characters—the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis is a fundamental problem with many interesting applications. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this paper, we propose a neural network model that also incorporates user behavioral information within a given document (tweet). The neural network used in this paper is a Convolutional Neural Network (CNN). The system is evaluated on two datasets provided by the SemEval-2016 Workshop. The proposed model outperforms current baseline models (including Naive Bayes and Support Vector Machines), which shows that going beyond the content of a document (tweet) is beneficial in sentiment classification, because it provides the classifier with a deep understanding of the task. [ABSTRACT FROM AUTHOR]

Abstract This paper presents an exploratory memetic perspective on the diffusion pattern of the ALS Ice Bucket Challenge. More precisely, the paper contributes to research on social learning, cultural evolution, and social contagion by shedding light on endogenous (meme-related) as well as exogenous (structural) properties that may have influenced the Ice Bucket Challenge's diffusion. In a first pillar, we present a descriptive memetic analysis of the diffusion pattern, including an evaluation of the Ice Bucket Challenge according to memetic criteria for successful replication. In the second pillar, we present an agent-based simulation model designed to illuminate the influence of particular social network characteristics on the Ice Bucket Challenge's diffusion. By combining these two pillars, we contribute to the advancement of memetic theory, narrowing the gap between a solely meme-centered perspective and social network analysis. [ABSTRACT FROM AUTHOR]

Abstract In order to enhance the effectiveness of the research on the new-type business management of online labour service crowdsourcing effect based on sharing economy, this paper proposes an online labour service crowdsourcing effect analysis method based on linear discriminant regression. Firstly, it relies on knowledge service and business combination to promote the selection coordination of public users and crowdsourcing website in sharing value-driving and spacial technology, wherein, the value chain of crowdsourcing is the value network composed of infrastructure and operation process, it promotes the communication technology product or technical service through the product flow, service flow, information flow and capital flow of value network and establishes the research model; secondly, based on linear discriminant regression, it measures and tests the relationship between online labour service crowdsourcing effect and single class by aid of the nearest subspace classifier, based on the relationship between test effect and training effect obtained from the farthest subspace classifier, finally, it verifies the effectiveness of the algorithm through simulated experiment. [ABSTRACT FROM AUTHOR]

Abstract Video summarization is one of the key techniques to access and manage a huge chunk of videos. Video summarization is used to extract the effective contents of a video sequence to generate a concise representation of its content. The involvement of crowdsourcing models in recent years is effectively used by researchers in E-commerce domain to increase the quality of the data contents. In this paper, we present a video summarization framework based on users emotion while they watch videos by analyzing cerebral activities through Electroencephalogram (EEG) signals. Three emotions, namely happy, sad and neutral have been extracted from the EEG signals. Video frames have been synchronized with EEG signals and tagged with various emotions. Finally, a crowdsourcing model has been used for effective summarization of the videos. The qualitative assessment of video summarization has been conducted with the help of user ratings using online Google Forms application. EEG signals of 28 users have been recorded while video streams of different emotions. An average accuracy of 83.93% has been recorded in emotion classification using crowdsourcing. Output summarized videos include dynamic video skims and the corresponding audio stream for better understanding. [ABSTRACT FROM AUTHOR]