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This paper presents a new technique for preparing word templates to improve the performance of dynamic time warping based keyword spotting. The proposed technique selects one reference template from a small set of examples and in contrast to existing model based approaches does not require extensive training
method based on semi-supervised learning to get focuses of social topics in a large amount of text. We develop a novel keyword extraction method named NATF-PDF, which is based on TFPDF algorithm, combined with supervised learning theory for keyword extraction. We compare its performance with TFIDF in comparison, and the
sharing a very lucrative venue for advertising. The recent $240 million investment by Microsoft in Facebook clearly reaffirms the opportunity in targeted advertising for online social networks. Content-targeted advertisement programs such as Google AdSense and Yahoo Contextual Match work by automatically spotting keywords in
information is deficient and noisy on YouTube. In this paper, we propose the novel dual updating method for YouTube video topic discovery. We first enhance the document representation for each video with its related videos, then we extract meaningful topics via keyword cores, at last, the video response links and the
In this paper we present an approach, inspired by honey bees, that allows us to take a glance at current events by exploring a portion of the Web and extracting keywords, relevant to current news stories. Not unlike the bees, that cooperate together to retrieve little bits of food, our approach uses agents to select
empirical rules, then, burst detection algorithm is adopted to discover peak interval of all phases, finally, we use a summarization technique TextRank to extract keywords from contents to summarize the topics in each phase. In addition, we perform experiments on two real-world datasets collected from different social media
query, in terms of keyword(s) to describe the query topic, while using only the citation graph and the keywords associated with the articles, and no latent information. We use a novel keyword expansion step, inspired by community finding in social network analysis, in DiSCern to ensure that the semantically correlated
appearance characteristics, so called visual features. This paper proposes a method to cluster the scientific documents based on visual features, so called VF-Clustering algorithm. Five kinds of visual features of documents are de-fined, including body, abstract, subtitle, keyword and title. The thought of crossover and
Recommender Systems are information filtering systems that guide the users in selecting the desired items based on the past user-item transactions. Recommender Systems have become the vital role in recent years and are utilized widely in various areas of social importance. The proposed work aims in recommending the
factorization with concept-based features is significantly lower than the error with standard keyword-based features. Qualitative evaluations also suggest that concept-based features yield more coherent, distinctive and interesting story forms compared to those produced by using standard keyword-based features.
With tags widely used in organizing and searching contents in massive data era, how to automatically generate appropriate tags of resource for users became a hot issue on social networks research. Tag recommendation for text resource can be modeled as a keyword extraction problem, hence topic modeling such as LDA
The subliminal impact of framing of social, political and environmental issues such as climate change has been studied for long time in political science and communications research. Media framing offers "interpretative package" for average citizens on how to make sense of climate change and its consequences to their
Twitter, a well-liked online social networking site, facilitates millions of users on a daily basis to dispatch and orate quick 140-character notes named tweets. Nowadays, Twitter is cogitated as the fastest and popular intermediate of communication and is used to follow latest events. Tweets pertaining to a specific
Collaborative tagging systems have recently emerged as a powerful way to label and organize large collections of data. The informal social classification structure in these systems, also known as folksonomy, provides a convenient way to annotate resources by allowing users to use any keyword or tag that they find
As a social bookmark tool, Folksonomy gives high freedom to users and allows users to share and notate resources. However, many tags applied arbitrarily by users can not really reflect the contents of web pages and lead to ineffectiveness in information retrieval. Moreover, there are still some important tasks about
Social data from online social networks is expanding rapidly as the number of users and articles posted increases, making public opinion analysis a greater challenge. Real-time topic detection is a key part of public opinion analysis. The complex data processing involved in traditional clustering and text
Twitter is a public social service that allows users to share information as short-text messages. Previous researchers have tried to analyze the information available on Twitter to discover topic trending. However, these topics are associated with the whole network, and are not associated to a particular place. In
generalized concepts representation of text (1) overcomes surface level differences (which arise when different keywords are used for related concepts) without drift, (2) leads to a higher-level semantic network representation of related stories, and (3) when used as features, they yield a significant 36% boost in performance
In this paper, we introduce and evaluate a framework for a user profile-based socializing application. We model the user profile as an unordered set composed of n keywords that represent users' preferences. We evaluate the effectiveness of two clustering algorithms, k-means and spectral clustering in the scope of
Unlike traditional multimedia content, content generated on social media platforms such as YouTube, Flickr etc are usually annotated with rich set of social tags such as keywords, textual description, category information, author's profile etc. In this paper we investigate the use of such social tag information for
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