2021
DOI: 10.1371/journal.pone.0260761
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An investigation into the deep learning approach in sentimental analysis using graph-based theories

Abstract: Sentiment analysis is a branch of natural language analytics that aims to correlate what is expressed which comes normally within unstructured format with what is believed and learnt. Several attempts have tried to address this gap (i.e., Naive Bayes, RNN, LSTM, word embedding, etc.), even though the deep learning models achieved high performance, their generative process remains a “black-box” and not fully disclosed due to the high dimensional feature and the non-deterministic weights assignment. Meanwhile, g… Show more

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Cited by 3 publications
(2 citation statements)
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“…The paper proposes a graph-based model to investigate electronic site reviews [45]. The research [46] proposes a transparent DNN model for the sentiment classifier. Acknowledge-specific parser for efficient generalization and extraction of multiword expressions amid English text is proposed [47].…”
Section: Graph-theoretic Based Approachmentioning
confidence: 99%
“…The paper proposes a graph-based model to investigate electronic site reviews [45]. The research [46] proposes a transparent DNN model for the sentiment classifier. Acknowledge-specific parser for efficient generalization and extraction of multiword expressions amid English text is proposed [47].…”
Section: Graph-theoretic Based Approachmentioning
confidence: 99%
“…Sentiment analysis is a subfield of natural language analytics that seeks to correlate the generally presented in an unstructured fashion with belief and acknowledgment [13]. Sentiment analysis, often known as opinion mining, is a natural language processing technique for interpreting and categorizing emotions in subjective data such as emails, social media posts, and survey results.…”
Section: Sentiment Analysismentioning
confidence: 99%