[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119879-en":3,"doc-seo-119879-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119879,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers - Article","This paper presents sentiment analysis combining lexicon-based and machine learning approaches in Turkish to assess public mood for predicting stock market behavior in BIST30 (Borsa Istanbul). The study collects 17,189 Turkish tweets from November 7–15, 2022 using MAXQDA 2020, labels polarities with a multilingual sentiment resource in Orange, and discards neutral labels for supervised learning. Six Python (sklearn) classifiers are tested on 9,076 positive/negative samples. Results show Support Vector Machine and Multilayer Perceptron achieve the highest performance (0.89/0.88 accuracy; AUC 0.8729/0.8647), while other models reach about 78.5%.","Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers  \nArticle  \nPublished Version  \nCreative Commons: Attribution-Noncommercial-No Derivative Works 4.0  \nOpen Access  \nCam, H. , Cam, A. V. , Demirel, U. and Ahmed, S. (2024) Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers. Heliyon, 10 (1) . e23784 . ISSN 2405-8440 doi: [https://doi.org/10.1016/j.heliyon.2023.e23784](https://doi.org/10.1016/j.heliyon.2023.e23784)  \nAvailable at [https://centaur. reading.ac. uk/1](https://centaur. reading.ac. uk/1) 14845/  \nIt is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing.  \nTo link to this article DOI: [http://dx.doi.org/10.1016/j. heliyon.2023.e23784](http://dx.doi.org/10.1016/j. heliyon.2023.e23784)  \nPublisher: Elsevier  \nAll outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders . Terms and conditions for use of this material are defined in the End User Agreement  .  \n[www. reading.ac. uk/centaur](www. reading.ac. uk/centaur)  \nCentAUR  \nCentral Archive at the University of Reading  \nReading’s research outputs online  \nHeliyon 10 (2024) e23784  \nContents lists available at ScienceDirect  \nHeliyon  \njournal [homepage: www.cell.com/heliyon](homepage: www.cell.com/heliyon)  \n| Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers\u003Cbr>Handan Cam a, Alper Veli Camb, Ugur Demirel c, **, Sana Ahmed d, *\u003Cbr>a Department of Management Information Systems, Faculty of Economic and Administrative Science, Gumushane University, 29000, Gumushane, Turkey\u003Cbr>b Department of Health Care Management, Faculty of Health Sciences, Gumushane University, 29000, Gumushane, Turkey\u003Cbr>c Irfan Can Kose Vocational School, Gumushane University, 29000, Gumushane, Turkey d Henley Business School, University of Reading, Reading, RG6 6AH, UK |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Sentiment analysis Natural language processing Machine learning\u003Cbr>Stock market\u003Cbr>Twitter |  | This paper presents a sentiment analysis combining the lexicon-based and machine learning (ML) -based approaches in Turkish to investigate the public mood for the prediction of stock market behavior in BIST30, Borsa Istanbul. Our main motivation behind this study is to apply sentiment analysis to financial-related tweets in Turkish. We import 17189 tweets posted as \"\\#Borsaistanbul, \\#Bist, \\#Bist30, \\#Bist100″ on Twitter between November 7, 2022, and November 15, 2022, via a MAXQDA 2020, a qualitative data analysis program. For the lexicon-based side, we use a multilingual sentiment offered by the Orange program to label the polarities of the 17189 samples as positive, negative, and neutral labels. Neutral labels are discarded for the machine learning experiments. For the machine learning side, we select 9076 data as positive and negative to implement the classification problem with six different supervised machine learning classifiers conducted in Python 3.6 with the sklearn library. In experiments, 80 % of the selected data is used for the training phase and the rest is used for the testing and validation phase. Results of the experiments show that the Support Vector Machine and Multilayer Perceptron classifier perform better than other classifiers with 0.89 and 0.88 accuracy and AUC values of 0.8729 and 0.8647 respectively. Other classifiers obtain approximately a 78,5 % accuracy rate. It is possible to increase sentiment analysis accuracy with parameter optimization on a larger, cleaner, and more balanced dataset by changing the pre-processing steps. This work can be expanded in the future to develop better sentiment analysis using deep learning approaches. |\n\n1. Introduction  \nThe increasing use of social media platforms has made it possible for peo","cbCaiu9tEvrtYLE3","https://ap.wps.com/l/cbCaiu9tEvrtYLE3","pdf",4720902,1,17,"English","en",105,"# Abstract\n# Introduction\n## Motivation\n## Problem statement and study aim","[{\"question\":\"What data does the study use for sentiment analysis, and how is it collected?\",\"answer\":\"The study imports 17,189 Turkish tweets containing hashtags such as #Borsaistanbul, #Bist, #Bist30, and #Bist100. Tweets are collected on Twitter between November 7, 2022 and November 15, 2022 using MAXQDA 2020.\"},{\"question\":\"How do the researchers build the machine learning training data?\",\"answer\":\"Sentiment labels are assigned using a multilingual sentiment resource in Orange. Neutral labels are discarded, and the remaining positive and negative tweets are used to create a classification dataset of 9,076 samples.\"},{\"question\":\"Which machine learning classifiers perform best and what metrics are reported?\",\"answer\":\"Support Vector Machine and Multilayer Perceptron achieve the best results. They report 0.89 and 0.88 accuracy and AUC values of 0.8729 and 0.8647, respectively.\"}]","Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers - Article | PDF",1785726810,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"sentiment-analysis-of-financial-twitter-posts-on-twitter-with-the-machine-learning-classifiers-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/sentiment-analysis-of-financial-twitter-posts-on-twitter-with-the-machine-learning-classifiers-article/119879/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data does the study use for sentiment analysis, and how is it collected?","Question",{"text":75,"@type":76},"The study imports 17,189 Turkish tweets containing hashtags such as #Borsaistanbul, #Bist, #Bist30, and #Bist100. Tweets are collected on Twitter between November 7, 2022 and November 15, 2022 using MAXQDA 2020.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the researchers build the machine learning training data?",{"text":80,"@type":76},"Sentiment labels are assigned using a multilingual sentiment resource in Orange. Neutral labels are discarded, and the remaining positive and negative tweets are used to create a classification dataset of 9,076 samples.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning classifiers perform best and what metrics are reported?",{"text":84,"@type":76},"Support Vector Machine and Multilayer Perceptron achieve the best results. They report 0.89 and 0.88 accuracy and AUC values of 0.8729 and 0.8647, respectively.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]