{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T22:31:50Z","timestamp":1770071510623,"version":"3.49.0"},"reference-count":13,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,12,16]]},"abstract":"<jats:p>Recently, many pre-trained text embedding models have been applied to effectively extract latent features from texts and achieve remarkable performance in various downstream tasks of sentiment analysis domain. However, these pre-trained text embedding models also encounter limitations related to the capability preserving the syntactical structure as well as the global long-range dependent relationships of words. Thus, they might fail to recognize the relevant syntactical features of words as valuable evidences for analyzing sentiment aspects. To overcome these limitations, we proposed a novel deep semantic contextual embedding technique for sentiment analysis, called as: SE4SA. Our proposed SE4SA is a multi-level text embedding model which enables to jointly exploit the long-range syntactical and sequential representations of texts. Then, these achieved rich semantic textual representations can support to have a better understanding on the sentiment aspects of the given text corpus, thereby resulting the better performance on sentiment analysis task. Extensive experiments in several benchmark datasets demonstrate the effectiveness or our proposed SE4SA model in comparing with recent state-of-the-art model.<\/jats:p>","DOI":"10.3233\/jifs-211535","type":"journal-article","created":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T11:10:27Z","timestamp":1631013027000},"page":"7527-7546","source":"Crossref","is-referenced-by-count":2,"title":["SE4SA: a deep syntactical contextualized text representation learning approach for sentiment analysis"],"prefix":"10.1177","volume":"41","author":[{"given":"Tham","family":"Vo","sequence":"first","affiliation":[{"name":"Thu Dau Mot University, Binh Duong, Vietnam"}]}],"member":"179","reference":[{"issue":"6","key":"10.3233\/JIFS-211535_ref1","doi-asserted-by":"crossref","first-page":"6025","DOI":"10.3233\/JIFS-169843","article-title":"A fuzzy convolutional neural network for text sentiment analysis","volume":"35","author":"Nguyen","year":"2018","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"issue":"5","key":"10.3233\/JIFS-211535_ref2","doi-asserted-by":"crossref","first-page":"2849","DOI":"10.3233\/JIFS-169472","article-title":"Actionable pattern discovery for Sentiment Analysis on Twitter Data in clustered environment","volume":"34","author":"Ranganathan","year":"2018","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"issue":"6","key":"10.3233\/JIFS-211535_ref3","doi-asserted-by":"crossref","first-page":"4335","DOI":"10.1007\/s10462-019-09794-5","article-title":"Sentiment analysis using deep learning architectures: a review","volume":"53","author":"Yadav","year":"2020","journal-title":"Artificial Intelligence Review"},{"key":"10.3233\/JIFS-211535_ref8","doi-asserted-by":"crossref","first-page":"23253","DOI":"10.1109\/ACCESS.2017.2776930","article-title":"Deep convolution neural networks for twitter sentiment analysis","volume":"6","author":"Jianqiang","year":"2018","journal-title":"IEEE Access"},{"issue":"4","key":"10.3233\/JIFS-211535_ref11","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s12559-018-9549-x","article-title":"Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis","volume":"10","author":"Ma","year":"2018","journal-title":"Cognitive Computation"},{"issue":"3","key":"10.3233\/JIFS-211535_ref28","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s10115-016-0993-1","article-title":"A semi-supervised approach to sentiment analysis using revised sentiment strength based on SentiWordNet","volume":"51","author":"Khan","year":"2017","journal-title":"Knowledge and information Systems"},{"key":"10.3233\/JIFS-211535_ref30","doi-asserted-by":"crossref","first-page":"20617","DOI":"10.1109\/ACCESS.2017.2740982","article-title":"A pattern-based approach for multi-class sentiment analysis in Twitter","volume":"5","author":"Bouazizi","year":"2017","journal-title":"IEEE Access"},{"issue":"3","key":"10.3233\/JIFS-211535_ref31","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1016\/j.eswa.2014.08.036","article-title":"A multi-label classification based approach for sentiment classification","volume":"42","author":"Liu","year":"2015","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JIFS-211535_ref32","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.eswa.2016.10.065","article-title":"Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN","volume":"72","author":"Chen","year":"2017","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JIFS-211535_ref36","doi-asserted-by":"crossref","first-page":"51522","DOI":"10.1109\/ACCESS.2019.2909919","article-title":"Sentiment analysis of comment texts based on BiLSTM","volume":"7","author":"Xu","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-211535_ref37","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.ins.2019.03.076","article-title":"Target-aware convolutional neural network for target-level sentiment analysis","volume":"491","author":"Hyun","year":"2019","journal-title":"Information Sciences"},{"key":"10.3233\/JIFS-211535_ref38","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.future.2018.12.018","article-title":"Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter","volume":"95","author":"Abid","year":"2019","journal-title":"Future Generation Computer Systems"},{"issue":"2","key":"10.3233\/JIFS-211535_ref39","doi-asserted-by":"crossref","first-page":"2201","DOI":"10.3233\/JIFS-179884","article-title":"Sentiment analysis in Nepali: Exploring machine learning and lexicon-based approaches","volume":"39","author":"Piryani","year":"2020","journal-title":"Journal of Intelligent & Fuzzy Systems"}],"container-title":["Journal of Intelligent &amp; 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