{ "ok": true, "doi": "10.46243/jst.2021.v6.i02.pp146-153", "doi_display": "10.46243/jst.2021.v6.i02.pp146-153", "doi_url": "https://doi.org/10.46243/jst.2021.v6.i02.pp146-153", "state": "registered", "url": "https://www.jst.org.in/index.php/pub/article/view/681", "title": "TWITTER STATISTICS EMOTION EVALUATION EVALUATION OF DEEP LEARNING METHODS", "version": 2, "registered_via": "crossref", "prefix": { "prefix": "10.46243", "status": "live" }, "registrant": { "name": "Longman Publishers", "kind": "publisher", "country": "India" }, "reserved_at": null, "registered_at": "2026-09-29 22:00:41", "updated_at": "2026-09-30 00:00:01", "withdrawn_at": null, "withdrawn_reason": null, "record": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2021.v6.i02.pp146-153", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "TWITTER STATISTICS EMOTION EVALUATION EVALUATION OF DEEP LEARNING METHODS", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2021.v6.i02.pp146-153" } ], "agents": [ { "role": "author", "name": { "given": "Dr.P.JOHN", "family": "PAUL" }, "sequence": "first" }, { "role": "author", "name": { "given": "N.KALYAN", "family": "GOUD" }, "sequence": "additional" }, { "role": "author", "name": { "given": "SAMYA", "family": "BADAVATH" }, "sequence": "additional" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2021-05-01", "date_type": "PublicationDate", "online": "2021-05-01" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "06", "issue": "03", "pages": { "first": "146", "last": "153" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/681", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/681/626", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/681/2927", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://www.jst.org.in/index.php/pub/article/view/681/626", "purpose": "similarity-checking" } ], "abstract": { "value": "This analysis compares and contrasts a variety of methods for assessing emotions in Twitter data. Deep learning (DL) methods have gained momentum in this field among academics, who collaborate on a level playing field to tackle a wide variety of problems. CNNs, which are used to locate pictures, and recurrent neural networks (RNNs), which may be utilized successfully in natural language processing (NLP), are two types of neural networks. For this reason, two types of neural networks are explicitly utilized. These images are used to assess and compare CNN ensembles and variants, as well as RNN category networks with long-term memory (LSTM). We also associate clothing with the type phrase embedding structures Word2Vec and the global phrase representation vectors (Glove). To put these methods to the test, we utilized information from the Seminal (Seminal), one of the most well-known international workshops on the internet. Different trials and combinations are used, and the better results for each variation are linked to their average efficiency. This study adds to the area of sentiment analysis by assessing the outcomes, benefits, and drawbacks of various methods using an evaluation method that use a single testing system for the same dataset and machine configuration.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2021-05-01", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1561/1500000011", "unstructured": "L. L. Bo Pang, «Opinion Mining and Sentiment Analysis Bo», Found. Trends® Inf. Retr., vol. 1, no. 2, pp. 91–231, 2008" }, { "key": "ref2", "doi": "10.1007/bf00344251", "unstructured": "K. Fukushima, «Neocognition: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position», Biol. Cybern., vol. 202, no. 36, pp. 193–202, 1980" }, { "key": "ref3", "doi": "10.1007/3-540-46805-6_19", "unstructured": "Y. Lecun, P. Ha, L. Bottou, Y. Bengio, S. Drive, eta R. B. 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Ray eta A. Chakrabarti, «A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis», Appl. Comput. Informatics, 2019" } ], "record": { "registrant": "Longman Publishers", "registered": "2026-09-12", "updated": "2026-09-20", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "record_sha256": "3ab48e27b04bed2b3539b4a05cbc4e35f706b5cd1ca06e11a055c61129f9f549", "handle": { "synced_at": "2026-10-01 18:13:54", "url": "https://www.jst.org.in/index.php/pub/article/view/681" }, "links": { "record_page": "https://registry.smartscholars.in/record.php?doi=10.46243%2Fjst.2021.v6.i02.pp146-153", "system_metadata": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2021.v6.i02.pp146-153&as=system", "history": "https://registry.smartscholars.in/api.php?action=history&doi=10.46243%2Fjst.2021.v6.i02.pp146-153", "kernel_xml": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2021.v6.i02.pp146-153&as=xml" } }