{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:13:12Z","timestamp":1758269592582,"version":"3.40.5"},"reference-count":0,"publisher":"IGI Global","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,4,1]]},"abstract":"<p>In the contemporary world, people share their thoughts rapidly in social media. Mining and extracting knowledge from this information for performing sentiment analysis is a complex task. Even though automated machine learning algorithms and techniques are available, and extraction of semantic and relevant key terms from a sparse representation of the review is difficult. Word embedding improves the text classification by solving the problem of sparse matrix and semantics of the word. In this paper, a novel architecture is proposed by combining long short-term memory (LSTM) with word embedding to extract the semantic relationship between the neighboring words and also a weighted self-attention is applied to extract the key terms from the reviews. Based on the experimental analysis on the IMDB dataset, the authors have shown that the proposed architecture word-embedding self-attention LSTM architecture achieved an F1 score of 88.67%, while LSTM and word embedding LSTM-based models resulted in an F1 score of 84.42% and 85.69%, respectively.<\/p>","DOI":"10.4018\/ijaci.2021040103","type":"journal-article","created":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T14:13:29Z","timestamp":1617027209000},"page":"33-52","source":"Crossref","is-referenced-by-count":17,"title":["Analysis of Sentiment on Movie Reviews Using Word Embedding Self-Attentive LSTM"],"prefix":"10.4018","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5073-8949","authenticated-orcid":true,"given":"Soubraylu","family":"Sivakumar","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6570-483X","authenticated-orcid":true,"given":"Ratnavel","family":"Rajalakshmi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India"}]}],"member":"2432","container-title":["International Journal of Ambient Computing and Intelligence"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=275757","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T21:55:04Z","timestamp":1651874104000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJACI.2021040103"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2021,4,1]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,4]]}},"URL":"https:\/\/doi.org\/10.4018\/ijaci.2021040103","relation":{},"ISSN":["1941-6237","1941-6245"],"issn-type":[{"type":"print","value":"1941-6237"},{"type":"electronic","value":"1941-6245"}],"subject":[],"published":{"date-parts":[[2021,4,1]]}}}