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A Recurrent Neural Network for Sentiment Quantification [article]

Andrea Esuli, Alejandro Moreo Fernández, Fabrizio Sebastiani
2018 pre-print
We propose a recurrent neural network architecture for quantification (that we call QuaNet) that observes the classification predictions to learn higher-order "quantification embeddings", which are then  ...  devised for it.  ...  ACKNOWLEDGMENTS The first author thanks NVidia corp. for granting a Titan X GPU.  ... 
doi:10.1145/3269206.3269287 arXiv:1809.00836v1 fatcat:wazieknoxzalxfbjudn6hv7une

DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis

Christos Baziotis, Nikos Pelekis, Christos Doulkeridis
2017 Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)  
We participated in all subtasks for English tweets, involving message-level and topic-based sentiment polarity classification and quantification.  ...  Also, we present a text processing tool suitable for social network messages, which performs tokenization, word normalization, segmentation and spell correction.  ...  Recurrent Neural Networks A more natural choice is to use Recurrent Neural Networks (RNN). An RNN processes an input sequentially, in a way that resembles how humans do it.  ... 
doi:10.18653/v1/s17-2126 dblp:conf/semeval/BaziotisPD17a fatcat:nnil3rgb2ve7lgrbulm7lvuwnq

YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter

Ming Wang, Biao Chu, Qingxun Liu, Xiaobing Zhou
2017 Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)  
We first trained a gated recurrent neural network using pre-trained word embeddings, then we extracted features from GRU layer and input these features into support vector machine to fulfill both the classification  ...  and quantification subtasks.  ...  The gated recurrent network proposed in (Bahdanauetal., 2014) is a recurrent neural network (a neural network with feedback connection, see (Atiya and Parlos, 2000) ) where the activation hj of the neural  ... 
doi:10.18653/v1/s17-2119 dblp:conf/semeval/WangCLZ17 fatcat:gtx5u2lwzbdsxjlox66lwqprxa

ELiRF-UPV at SemEval-2017 Task 4: Sentiment Analysis using Deep Learning

José-Àngel González, Ferran Pla, Lluís-F. Hurtado
2017 Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)  
Our approach is based on the use of convolutional and recurrent neural networks and the combination of general and specific word embeddings with polarity lexicons.  ...  We participated in all of the proposed subtasks both for English and Arabic languages using the same system with small variations.  ...  Acknowledgements This work has been funded by the MINECO and FEDER founds under TIN2014-54288-C4-3-R project: ASLP-MULAN: Audio, Speech and Language Processing for Multimedia Analytics.  ... 
doi:10.18653/v1/s17-2121 dblp:conf/semeval/GonzalezPH17 fatcat:xhbfv62xkngghkigagzs343zte

Combination of Recursive and Recurrent Neural Networks for Aspect-Based Sentiment Analysis Using Inter-Aspect Relations

Cem Rifki Aydin, Tunga Gungor
2020 IEEE Access  
In this paper, we propose a novel neural network framework that combines recurrent and recursive neural models for aspect-based sentiment analysis.  ...  There are only a few studies that incorporate both of these models into a single neural network for the sentiment classification task.  ...  RECURRENT NEURAL NETWORK MODELS In sentiment analysis, a large number of studies use recurrent neural networks due to their ability to model sequence data.  ... 
doi:10.1109/access.2020.2990306 fatcat:4biow2zv4rcqvmpmrqa2rzi67a

NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture

Nikolay Karpov
2017 Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)  
Basically, our study was aimed to analyze the effectiveness of a mixture of quantification technique with one of deep learning architecture.  ...  In this case, the task is to correctly estimate proportions of each sentiment expressed in the set of documents (quantification task).  ...  Acknowledgments The reported study was funded by RFBR under research Project No. 16-06-00184 A, the Academic Fund Program at the National Research University Higher School of Economics (HSE) in 2017 (grant  ... 
doi:10.18653/v1/s17-2113 dblp:conf/semeval/Karpov17 fatcat:uvbcyjdedfg25lcoroauzrmuja

Rocchio Nearest Centroid and Normalized Neural Network based Lead Generation in Social Media Marketing

K.S. Narayanan, Dr. S. Suganya
2021 Revista GEINTEC  
Next an Arbitrary Normalized Attention-based Recurrent Neural Network Lead Generation algorithm is designed aggregating characterizations from preceding and succeeding tweets while generating lead via  ...  We introduce Rocchio Nearest Centroid Laplacian Feature Selection model that combines Rocchio Nearest Centroid and Laplace function for selecting relevant features or tweets.  ...  A novel Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN)-grid search-based deep neural network model for sentiment analysis was proposed in[10].  ... 
doi:10.47059/revistageintec.v11i4.2302 fatcat:msow6ypx2jhf7ixojebzuu4n2y

A Feature-Based Approach for Sentiment Quantification Using Machine Learning

Kashif Ayyub, Saqib Iqbal, Muhammad Wasif Nisar, Ehsan Ullah Munir, Fawaz Khaled Alarfaj, Naif Almusallam
2022 Electronics  
Sentiment quantification, a new research problem in this field, extends sentiment analysis from individual documents to an aggregated collection of documents.  ...  Sentiment analysis has been widely researched, but sentiment quantification has drawn less attention despite offering a greater potential to enhance current business intelligence systems.  ...  (DBN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN).  ... 
doi:10.3390/electronics11060846 fatcat:vobfnyum5vd6fgjvipu73qxziu

SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELS

Nehal Mohamed Ali, Marwa Mostafa Abd El Hamid, Aliaa Youssif
2019 International Journal of Data Mining & Knowledge Management Process  
Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies  ...  Multilayer Perceptron (MLP) was developed as a baseline for other networks results.  ...  For inputs elements of { (0) , … , { ( ) } Where, ( ) ∈ ( ) and ℎ ( ) ∈ ( ) as the hidden layer of the Recurrent Neural Networks for time t.  ... 
doi:10.5121/ijdkp.2019.9302 fatcat:tv6tkmctsvdx7o3whyi5oko3ie

NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis

Samhaa R. El-Beltagy, Mona El kalamawy, Abu Bakr Soliman
2017 Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)  
The first classifier was a convolutional neural network for which we trained (word2vec) word embeddings.  ...  For subtask A, we made use of our previously developed sentiment analyzer which we augmented with a scored lexicon. For subtasks B and D, we used an ensemble of three different classifiers.  ...  The Finki team for example (Stojanovski et al. 2016 ) developed a system composed of a merged convolutional neural network with a gated recurrent neural network.  ... 
doi:10.18653/v1/s17-2133 dblp:conf/semeval/El-BeltagykS17 fatcat:zfihr543uveonkipzfrth7ss5m

NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis [article]

Samhaa R. El-Beltagy, Mona El Kalamawy, Abu Bakr Soliman
2017 arXiv   pre-print
The first classifier was a convolutional neural network for which we trained (word2vec) word embeddings.  ...  For subtask A, we made use of our previously developed sentiment analyzer which we augmented with a scored lexicon. For subtasks B and D, we used an ensemble of three different classifiers.  ...  The Finki team for example (Stojanovski et al. 2016 ) developed a system composed of a merged convolutional neural network with a gated recurrent neural network.  ... 
arXiv:1710.08458v1 fatcat:gihp3mluajgmlmnpm5ytpkewom

Exploring Diverse Features for Sentiment Quantification using Machine Learning Algorithms

Kashif Ayyub, Saqib Iqbal, Ehsan Ullah Munir, Muhammad Wasif Nisar, Momna Abbasi
2020 IEEE Access  
In this paper, we explore diverse feature sets and classifiers for sentiment quantification.  ...  INDEX TERMS Deep learning, feature engineering, lexical feature, machine learning, sentiment quantification.  ...  Tanh can be given as: tanh (x) = 2 1 + e −2x − 1 (13) 4) RNN Recurrent Neural Networks (RNN) is a class of artificial neural networks which are useful for unsegmented tasks such as handwriting or speech  ... 
doi:10.1109/access.2020.3011202 fatcat:uwv5ai4emzhq7euzvuv75afbr4

Visualizing and Understanding Neural Models in NLP

Jiwei Li, Xinlei Chen, Eduard Hovy, Dan Jurafsky
2016 Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies  
While neural networks have been successfully applied to many NLP tasks the resulting vector-based models are very difficult to interpret.  ...  In this paper we describe strategies for visualizing compositionality in neural models for NLP, inspired by similar work in computer vision.  ...  Similar strategy Stanford Sentiment Treebank Stanford Sentiment Treebank is a benchmark dataset widely used for neural model evaluations.  ... 
doi:10.18653/v1/n16-1082 dblp:conf/naacl/LiCHJ16 fatcat:j4e3qoacdzgiplw6ovf3mm7v3u

Studying Attention Models in Sentiment Attitude Extraction Task [chapter]

Nicolay Rusnachenko, Natalia Loukachevitch
2020 Lecture Notes in Computer Science  
In this paper, we provide a study on attention-based context encoders in the sentiment attitude extraction task.  ...  For this task, we adapt attentive context encoders of two types: (i) feature-based; (ii) self-based.  ...  Related Work In previous works, various neural network approaches for targeted sentiment analysis were proposed. In [10] the authors utilize convolutional neural networks (CNN).  ... 
doi:10.1007/978-3-030-51310-8_15 fatcat:d4uwtgna6zg3liwwjokqxrwo6u

SemEval-2016 Task 4: Sentiment Analysis in Twitter [article]

Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, Veselin Stoyanov
2019 arXiv   pre-print
The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet.  ...  The second variant focuses on the correct estimation of the prevalence of each class of interest, a task which has been called quantification in the supervised learning literature.  ...  All other teams used general-purpose approaches for single-label multi-class classification, in many cases relying (as for Subtask B) on convolutional neural networks, recurrent neural networks, and word  ... 
arXiv:1912.01973v1 fatcat:dmwtpuuturcyrk5ve4ocsiy4ry
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