A Sentiment Information Collector-Extractor Architecture Based Neural Network for Sentiment Analysis
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TLDR
A new ensemble strategy is applied to combine the results of different sub-extractors, making the SIE more universal and outperform any single sub- Extractor and outperforms the state-of-the-art methods on three datasets of different language.About:
This article is published in Information Sciences.The article was published on 2018-10-01 and is currently open access. It has received 21 citations till now. The article focuses on the topics: Sentiment analysis & Deep learning.read more
Citations
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Journal ArticleDOI
Carrying out consensual Group Decision Making processes under social networks using sentiment analysis over comparative expressions
Juan Antonio Morente-Molinera,Gang Kou,K. Samuylov,Raquel Ureña,Enrique Herrera-Viedma,Enrique Herrera-Viedma +5 more
TL;DR: This paper presents a novel model for experts to carry out Group Decision Making processes using free text and alternatives pairwise comparisons and introduces two ways of applying consensus measures over the Group decision Making process.
Journal ArticleDOI
A comparative study of machine translation for multilingual sentence-level sentiment analysis
TL;DR: This work evaluates existing efforts proposed to do language specific sentiment analysis with a simple yet effective baseline approach and suggests that simply translating the input text in a specific language to English and then using one of the existing best methods developed for English can be better than the existing language-specific approach evaluated.
Journal ArticleDOI
Convolution-deconvolution word embedding: an end-to-end multi-prototype fusion embedding method for natural language processing
TL;DR: In this paper, an end-to-end multi-prototype fusion embedding that fuses context-specific and task-specific information was proposed to solve the problem of polysemous-unaware word embedding.
References
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Proceedings Article
Business-aware visual concept discovery from social media for multimodal business venue recognition
TL;DR: A novel framework for business venue recognition that can incorporate semantic signals mined from business reviews for extracting semantic concept features from a query image and extends visually detected concepts to multimodal feature representations that allow a test image to be associated with business reviews and images from social media for business venues recognition.
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Business event curation: merging human and automated approaches
TL;DR: Preliminary work to construct a knowledge curation system exploits natural language processing techniques to automatically implement business event extraction, provides a user-facing interface to assist human curators, and a feedback loop to improve the performance of the Information Extraction Model for the automated parts of the system.
Proceedings ArticleDOI
Ensemble classifiers for spam review detection
TL;DR: This research is aimed at employing three base classifiers, Naïve Bayes, Support Vector Machines and Logistic Regression to form ensemble classifiers complimented with Arching classifier to provide a more improved and efficient classification of review spam.