2018
DOI: 10.1016/j.knosys.2018.04.006
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Sentiment classification with word localization based on weakly supervised learning with a convolutional neural network

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Cited by 101 publications
(41 citation statements)
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“…Lee et al applied class activation mapping to the task of sentiment classification [26]. They trained multi-channel CNNs on movie reviews, and then demonstrated sentiment-specific expressions mined by CAMs.…”
Section: Related Workmentioning
confidence: 99%
“…Lee et al applied class activation mapping to the task of sentiment classification [26]. They trained multi-channel CNNs on movie reviews, and then demonstrated sentiment-specific expressions mined by CAMs.…”
Section: Related Workmentioning
confidence: 99%
“…Word to Vector representation is the word vector model [37][38][39][40][41], which defines the words in the word space vector representation. This is the simple representation as those used in the text categorization.…”
Section: A Word To Vector Representationmentioning
confidence: 99%
“…This vector put unique index for each word except all zeros [51]. In literature, different word embedding techniques namely random vectors, Word2Vec, Glove (Global vectors for word representation), FastText [48] and Character vector [46] are experimented to construct input matrix for CNN classifier. Word2Vec is a predictive model [51].…”
Section: A Word Embedding Technique-word2vecmentioning
confidence: 99%