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This paper presents a method named SoSVMRank, which integrates the social information of a Web document to generate a high-quality summarization. In order to do that, the summarization was formulated as a learning to rank task, in which the order of a sentence or comment was determined by its informative information. The informative information was measured by a set of local and social features in...
Twitter sentiment analysis provides organizations with real-time monitoring of public feelings towards particular products and events related to them. Most existing research is focused on extraction of sentiment features through analysis of lexical and syntactic features that are expressed explicitly through words, emoticons, exclamation marks etc. Single machine learning classifiers are usually employed...
Fine-grained activity recognition focuses recognition on sub-ordinate levels. This task is made difficult due to low inter-class variability and high intra-class variability caused by human motion and objects. We propose that recognition of such activities can be significantly improved by grouping and decomposing them into a hierarchy of multiple abstraction layers; we introduce a Hierarchical Activity...
Twitter sentiment analysis offers organizations an ability to monitor public feeling towards the products and events related to them in real time. Most existing researches for Twitter sentiment analysis are focused on the extraction of sentiment feature of lexical and syntactic feature that are expressed explicitly through words, emoticons, exclamation marks etc, although sentiment implicitly expressed...
In this paper, we propose a novel approach for reader-emotion categorization using word embedding learned from neural networks and an SVM classifier. The primary objective of such word embedding methods involves learning continuous distributed vector representations of words through neural networks. It can capture semantic context and syntactic cues, and subsequently be used to infer similarity measures...
The unprecedented growth of data in web, social media and the attempt to make the cognitive process using computers make Sentiment Analysis a challenging and interesting research problem. Sentiment Analysis mainly deals with the process of analyzing the sentiments or feelings from someone's expression or piece of information, and also in discovering the cognitive behavior of humans. The usage of computers...
Multi-concept image query is a multi-label classification challenge. Traditional query methods focus on single concept query, and only use image visual data without considering the associated textual tag data. In this work, we address the problem of bimodal multi-concept image query, namely retrieving bimodal images with multiple target concepts from the image set. We propose a novel Bimodal Learning...
This paper studies the problem of end-to-end windows mining directly from detection output. Traditional object detection systems approach this problem in an ad-hoc manner, say, Non-Maximum Suppression (NMS). Beyond NMS, multi-class context modeling has been explored thoroughly recent years. But all these methods put their emphasis on eliminating false positive windows rather than improving recall...
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