517 research outputs found
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User sentiment detection: a YouTube use case
In this paper we propose an unsupervised lexicon-based approach to detect the sentiment polarity of user comments in YouTube. Polarity detection in social media content is challenging not only because of the existing limitations in current sentiment dictionaries but also due to the informal linguistic styles used by users. Present dictionaries fail to capture the sentiments of community-created terms. To address the challenge we adopted a data-driven approach and prepared a social media specific list of terms and phrases expressing user sentiments and opinions. Experimental evaluation shows the combinatorial approach has greater potential. Finally, we discuss many research challenges involving social media sentiment analysis
Extracting semantic entities and events from sports tweets
Large volumes of user-generated content on practically every major issue and event are being created on the microblogging site Twitter. This content can be combined and processed to detect events, entities and popular moods to feed various knowledge-intensive practical applications. On the downside, these content items are very noisy and highly informal, making it difficult to extract sense out of the stream. In this paper, we exploit various approaches to detect the named entities and significant micro-events from usersтАЩ tweets during a live sports event. Here we describe how combining linguistic features with background knowledge and the use of Twitter-specific features can achieve high, precise detection results (f-measure = 87%) in different datasets. A study was conducted on tweets from cricket matches in the ICC World Cup in order to augment the event-related non-textual media with collective intelligence
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Enriching videos with light semantics
This paper describes an ongoing prototypical framework to annotate and retrieve web videos with light semantics. The proposed framework reuses many existing vocabularies along with a video model. The knowledge is captured from three different information spaces (media content, context, document). We also describe ways to extract the semantic content descriptions from the existing usergenerated content using multiple approaches of linguistic processing and Named Entity Recognition, which are later identified with DBpedia resources to establish meanings for the tags. Finally, the implemented prototype is described with multiple search interfaces and retrieval processes. Evaluation on semantic enrichment shows a considerable (50% of videos) improvement in content description
Towards a continuous modeling of natural language domains
Humans continuously adapt their style and language to a variety of domains.
However, a reliable definition of `domain' has eluded researchers thus far.
Additionally, the notion of discrete domains stands in contrast to the
multiplicity of heterogeneous domains that humans navigate, many of which
overlap. In order to better understand the change and variation of human
language, we draw on research in domain adaptation and extend the notion of
discrete domains to the continuous spectrum. We propose representation
learning-based models that can adapt to continuous domains and detail how these
can be used to investigate variation in language. To this end, we propose to
use dialogue modeling as a test bed due to its proximity to language modeling
and its social component.Comment: 5 pages, 3 figures, published in Uphill Battles in Language
Processing workshop, EMNLP 201
A lightweight web video model with content and context descriptions for integration with linked data
The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud
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Using a lightweight multimedia content model for semantic annotation
In this paper we discuss the use of a multimedia content model for automatic extraction of semantic metadata from multimedia content. We developed a modular and extensible framework to model the content feature of multimedia data and also describe the way it can be integrated with other existing vocabularies. The goal of this model is to generate sufficient understanding of media content, its context and its relation to domain knowledge in order to perform multimedia reasoning. We implemented a tool that analyzes and links low-level descriptions to higher-level domain specific semantic concepts by means of statistical learning and clustering analysis. Experimental result shows the approach performs well in visual concept prediction in the image which can be further augmented with other information sources such as context text and or audio source
Exploratory Study of Ventilated Flows About Yawed Surface-Piercing Struts
Reported herein are the results of observations and measurements made in connection with a study of the phenomenon of the development of atmosphere-connected cavities about surface-piercing struts. Conditions for the existence of such ventilated flows which have been derived from the experimental data are presented. In addition, certain broad conclusions pertinent to model testing and full-scale design are reached. Further experimentation to define the inception of ventilation as a function of boundary-layer state or Reynolds number is required
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