Sentiment analysis, a hot research topic, presents new challenges for understanding users' opinions and judg-ments expressed online. They aim to classify the subjective texts by assigning them a polarity label. In th...
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Sentiment analysis, a hot research topic, presents new challenges for understanding users' opinions and judg-ments expressed online. They aim to classify the subjective texts by assigning them a polarity label. In this paper, weintroduce a novel machine learning framework using auto-encoders network to predict the sentiment polarity label at theword level and the sentence level. Inspired by the dimensionality reduction and the feature extraction capabilities of theauto-encoders, we propose a new model for distributed word vector representation "PMI-SA" using as input pointwise-mutual-information "PMI" word vectors. The resulted continuous word vectors are combined to represent a sentence. Anunsupervised sentence embedding method, called Contextual Recursive Auto-Encoders "CoRAE", is also developed forlearning sentence representation. Indeed, CoRAE follows the basic idea of the recursive auto-encoders to deeply composethe vectors of words constituting the sentence, but without relying on any syntactic parse tree. The CoRAE model consistsin combining recursively each word with its context words (neighbors' words: previous and next) by considering the wordorder. A support vector machine classifier with fine-tuning technique is also used to show that our deep compositionalrepresentation model CoRAE improves significantly the accuracy of sentiment analysis task. Experimental results demon-strate that CoRAE remarkably outperforms several competitive baseline methods on two databases, namely, Sanders twittercorpus and Facebook comments corpus. The CoRAE model achieves an efficiency of 83.28% with the Facebook dataset and97.57% with the Sanders dataset.
Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models ...
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This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously colle...
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Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA stor...
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The ubiquitous computing also called pervasive computing regroups the characteristic of mobile computing and the technique of context-awareness that are flexible, adaptable, and capable of acting autonomously on behal...
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The goal of the semantic web, also known as a third generation, is to improve the web service research, discovery, selection, composition and integration. The pervasive systems correspond to a complete communication f...
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Social network websites are mainly constructed around the notion of user identities, as set up on the bases of their profiles, and online generated contents such as texts, videos, photos. Still, while some profiles ga...
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People re-identification has been a very active research topic recently in computer vision. It is an important application in surveillance system with disjoint cameras. This paper is focused on the implementation of a...
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This position paper proposes a collaboration method between Semantic Web and Fuzzy Logic aiming to handle uncertainty in the information retrieval process in order to cover more relevant items in result of search proc...
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This position paper proposes a collaboration method between Semantic Web and Fuzzy Logic aiming to handle uncertainty in the information retrieval process in order to cover more relevant items in result of search process. The collaboration method employs OWL ontology in query enhancement, RDF in annotation process and fuzzy rules in ranking enhancement.
In this paper, we propose a spatial temporal video-text detection technique which proceed in two principal steps: potential text region detection and a filtering process. In the first step we divide dynamically each p...
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ISBN:
(纸本)1424417511
In this paper, we propose a spatial temporal video-text detection technique which proceed in two principal steps: potential text region detection and a filtering process. In the first step we divide dynamically each pair of consecutive video frames into sub block in order to detect change. A significant difference between homologous blocks implies the appearance of an important object which may be a text region. The temporal redundancy is then used to filter these regions and forms an effective text region. The experimentation driven on a variety of video sequences shows the effectiveness of our approach by obtaining a 89,39% as precision rate and 90,19 as recall.
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