A Sentiment Information Collector-Extractor Architecture Based Neural Network for Sentiment Analysis
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
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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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Book ChapterDOI
Mining Text Data
TL;DR: Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining.
Book ChapterDOI
Bidirectional LSTM networks for improved phoneme classification and recognition
TL;DR: In this paper, two experiments on the TIMIT speech corpus with bidirectional and unidirectional Long Short Term Memory networks are carried out and it is found that a hybrid BLSTM-HMM system improves on an equivalent traditional HMM system.
Posted Content
A C-LSTM Neural Network for Text Classification
TL;DR: C-LSTM is a novel and unified model for sentence representation and text classification that outperforms both CNN and LSTM and can achieve excellent performance on these tasks.
Proceedings ArticleDOI
Political Ideology Detection Using Recursive Neural Networks
TL;DR: A RNN framework is applied to the task of identifying the political position evinced by a sentence to show the importance of modeling subsentential elements and outperforms existing models on a newly annotated dataset and an existing dataset.
Proceedings Article
Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts
TL;DR: A jointed CNN and RNN architecture is described, taking advantage of the coarse-grained local features generated by CNN and long-distance dependencies learned via RNN for sentiment analysis of short texts.