PMID- 36714074 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20230501 IS - 0941-0643 (Print) IS - 1433-3058 (Electronic) IS - 0941-0643 (Linking) VI - 35 IP - 14 DP - 2023 TI - TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm. PG - 10311-10328 LID - 10.1007/s00521-023-08236-2 [doi] AB - COVID-19, a novel virus from the coronavirus family, broke out in Wuhan city of China and spread all over the world, killing more than 5.5 million people. The speed of spreading is still critical as an infectious disease, and it causes more and more deaths each passing day. COVID-19 pandemic has resulted in many different psychological effects on people's mental states, such as anxiety, fear, and similar complex feelings. Millions of people worldwide have shared their opinions on COVID-19 on several social media websites, particularly on Twitter. Therefore, it is likely to minimize the negative psychological impact of the disease on society by obtaining individuals' views on COVID-19 from social media platforms, making deductions from their statements, and identifying negative statements about the disease. In this respect, Twitter sentiment analysis (TSA), a recently popular research topic, is used to perform data analysis on social media platforms such as Twitter and reach certain conclusions. The present study, too, proposes TSA using convolutional neural network optimized via arithmetic optimization algorithm (TSA-CNN-AOA) approach. Firstly, using a designed API, 173,638 tweets about COVID-19 were extracted from Twitter between July 25, 2020, and August 30, 2020 to create a database. Later, significant information was extracted from this database using FastText Skip-gram. The proposed approach benefits from a designed convolutional neural network (CNN) model as a feature extractor. Thanks to arithmetic optimization algorithm (AOA), a feature selection process was also applied to the features obtained from CNN. Later, K-nearest neighbors (KNN), support vector machine, and decision tree were used to classify tweets as positive, negative, and neutral. In order to measure the TSA performance of the proposed method, it was compared with different approaches. The results demonstrated that TSA-CNN-AOA (KNN) achieved the highest tweet classification performance with an accuracy rate of 95.098. It is evident from the experimental studies that the proposed approach displayed a much higher TSA performance compared to other similar approaches in the existing literature. CI - (c) The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. FAU - Aslan, Serpil AU - Aslan S AUID- ORCID: 0000-0001-8009-063X AD - Department of Software Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, 44210 Malatya, Turkey. GRID: grid.507331.3. ISNI: 0000 0004 7475 1800 FAU - Kiziloluk, Soner AU - Kiziloluk S AD - Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, 44210 Malatya, Turkey. GRID: grid.507331.3. ISNI: 0000 0004 7475 1800 FAU - Sert, Eser AU - Sert E AD - Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, 44210 Malatya, Turkey. GRID: grid.507331.3. ISNI: 0000 0004 7475 1800 LA - eng PT - Journal Article DEP - 20230120 PL - England TA - Neural Comput Appl JT - Neural computing & applications JID - 9313239 PMC - PMC9867606 OTO - NOTNLM OT - Arithmetic optimization algorithm OT - COVID-19 OT - Convolutional neural network OT - Sentiment analysis OT - Twitter COIS- Conflict of interestAll authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. The article I have submitted to the journal for review is original, has been written by me and has not been published elsewhere. The images that I have submitted to the journal for review are original, were taken me, and have not been published elsewhere. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers' bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. EDAT- 2023/01/31 06:00 MHDA- 2023/01/31 06:01 PMCR- 2023/01/22 CRDT- 2023/01/30 04:24 PHST- 2022/04/26 00:00 [received] PHST- 2023/01/06 00:00 [accepted] PHST- 2023/01/31 06:01 [medline] PHST- 2023/01/31 06:00 [pubmed] PHST- 2023/01/30 04:24 [entrez] PHST- 2023/01/22 00:00 [pmc-release] AID - 8236 [pii] AID - 10.1007/s00521-023-08236-2 [doi] PST - ppublish SO - Neural Comput Appl. 2023;35(14):10311-10328. doi: 10.1007/s00521-023-08236-2. Epub 2023 Jan 20.