Cognitive Discourse Analysis can be up-scaled using Sentiment Analysis

Cognitive Discourse Analysis can be up-scaled using Sentiment Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cognitive Discourse Analysis can be up-scaled using Sentiment Analysis Leena Sarah Farhat, Simon Willcock, William John Teahan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7743985/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Language is a window into people's thoughts, attitudes, and worldviews, and analyses such as cognitive discourse analysis (CODA) can help reveal this. However, conducting CODA is often resource intensive, requiring considerable time and person-power. By contrast, natural language processing (NLP) approaches may potentially be able to replicate similar results using sentiment analysis for substantially reduced researcher effort. Here, we use respondent-validated data from Amazon product evaluations and Internet Movie Database (IMDB) movie ratings to contrast CODA against two NLP sentiment analysis tools (TextBlob and VADER). NLP approaches showed strong correlations with user-assigned sentiment scores in both data sets. For example, Textblob and VADER show Pearson correlations of r =0.4383 and 0.5188 respectively with the Amazon data and r = 0.594 and r = 0.4664 respectively with the IMDB data (all p < 0.001), while CODA approaches yielded lower correlations (Amazon: r =0.1357; IMDB: r =0.1708 ; p <0.001). Our results show that when it comes to sentiment analysis accuracy and dependability, NLP techniques routinely beat conventional CODA methodologies and, therefore, should be widely adopted, enabling CODA to be undertaken at scale. To help this, we provide an open-source widget \href{https://gitlab.com/lleleena/sasi/-/blob/main/README.md}{Survey Analysis for Sentiment (SASi)}, which reduces the technical requirements of implementing NLP techniques. Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Cognitive discourse analysis CODA 038 Sentimentanalysis SurveyAnalysis Freetextanaly-039 sis Natural language processing VADER TextBlob Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7743985","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":572240249,"identity":"2229b310-a713-4353-b014-aab113e47f47","order_by":0,"name":"Leena Sarah Farhat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACPgbGhw8S2A4w8EsgiTI24NHCxsBsbPABqEVyBglazCRnALUY3CBai3QzgzRP2Z0849vNzx5+qbjHwN9+gA3FSgwtMocZjHnOPSs2u3PM3FjmTDGDxJkENskN+LRI5B9I5m07nLjtRoKZtGRbAgPDDQY2yQd4tSQzHAZp2Twj/RtYizwRWhgbZwK1bJDIMZP8CNRiANKC32HJzAwfzh0ulrhzpkya4UwCj+GZxGZLfN7nl0hm/5FQdjiPf3b7NskfFQlycscPH7zZg0cLDCSACGYeBgYeArGCpoXxB1FqR8EoGAWjYKQBAC1ITmHAfklcAAAAAElFTkSuQmCC","orcid":"","institution":"Bangor University","correspondingAuthor":true,"prefix":"","firstName":"Leena","middleName":"Sarah","lastName":"Farhat","suffix":""},{"id":572240253,"identity":"a632d491-1c23-4750-9737-a508e985010f","order_by":1,"name":"Simon Willcock","email":"","orcid":"","institution":"Bangor University","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Willcock","suffix":""},{"id":572240260,"identity":"ea08feea-9e6e-48f1-ba6b-58d48c1e52fc","order_by":2,"name":"William John Teahan","email":"","orcid":"","institution":"Bangor University","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"John","lastName":"Teahan","suffix":""}],"badges":[],"createdAt":"2025-09-29 16:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7743985/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7743985/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100008847,"identity":"cf719e3f-efb5-455f-8cb6-1181e8b24890","added_by":"auto","created_at":"2026-01-12 05:57:39","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4986,"visible":true,"origin":"","legend":"","description":"","filename":"7cf268e6f0df4c4cae801ede1e20b6f0.json","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/4bc12d1ed32e30527bebc1fd.json"},{"id":100362077,"identity":"bc88dbb9-6e97-4fa2-80d7-051cf19461ce","added_by":"auto","created_at":"2026-01-16 07:46:10","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174927,"visible":true,"origin":"","legend":"","description":"","filename":"CODAnetworkcropped.png","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/c6a2cd8684df12adc416493a.png"},{"id":100008849,"identity":"c4c59b09-a8ba-4748-b560-fa8b9de21de2","added_by":"auto","created_at":"2026-01-12 05:57:39","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179593,"visible":true,"origin":"","legend":"","description":"","filename":"CODAtimelinecropped.png","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/74b45727d2dcaf2d12376de6.png"},{"id":100361781,"identity":"b9d611ec-3243-4b9a-8b6e-f5ca8153a5b1","added_by":"auto","created_at":"2026-01-16 07:45:43","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1235181,"visible":true,"origin":"","legend":"","description":"","filename":"CognitiveDiscourseAnalysiscanbeupscaledusingSentimentAnalysis.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/83a5e717d2efdb271384d5b3.pdf"},{"id":100008854,"identity":"7cecdd8f-e18f-4c9e-a558-eb1e0250b606","added_by":"auto","created_at":"2026-01-12 05:57:39","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136909,"visible":true,"origin":"","legend":"","description":"","filename":"Coverletter.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/d674fdaa066b7440a9356b46.pdf"},{"id":100362074,"identity":"6ea57001-705c-4582-9da2-98d14a8cd1e5","added_by":"auto","created_at":"2026-01-16 07:46:10","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":709310,"visible":true,"origin":"","legend":"","description":"","filename":"Graphssentimentanalysis.png","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/7097e55af4bc458a423fe347.png"},{"id":100008861,"identity":"fcb7fe5a-9986-4f9b-bcf8-8009bad04831","added_by":"auto","created_at":"2026-01-12 05:57:39","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161702,"visible":true,"origin":"","legend":"","description":"","filename":"Pointbypointresponse.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/b7681ba96e3b79786d732e58.pdf"},{"id":100008852,"identity":"04803419-3a44-493d-9645-b1ed8d86d861","added_by":"auto","created_at":"2026-01-12 05:57:39","extension":"sty","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11158,"visible":true,"origin":"","legend":"","description":"","filename":"acl.sty","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/22731b75bf717a194236342a.sty"},{"id":100008851,"identity":"3da89ad8-4524-4bd6-a0e7-c70ecb3b90a6","added_by":"auto","created_at":"2026-01-12 05:57:39","extension":"bst","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45185,"visible":true,"origin":"","legend":"","description":"","filename":"aclnatbib.bst","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/5b7bb6f8ecd14fff5360e82a.bst"},{"id":100008859,"identity":"57dc6087-7ee9-4f29-8efb-b71f56e3893f","added_by":"auto","created_at":"2026-01-12 05:57:39","extension":"pgf","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26307,"visible":true,"origin":"","legend":"","description":"","filename":"data.pgf","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/3396be577e2e00cad7ad00f4.pgf"},{"id":100008855,"identity":"f84ba919-0232-4f6d-b5ef-862602acfa6c","added_by":"auto","created_at":"2026-01-12 05:57:39","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73551,"visible":true,"origin":"","legend":"","description":"","filename":"pearson.png","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/4bffeb22d9f0f5aad269f273.png"},{"id":100361793,"identity":"fdb59e7b-63d6-4c00-9b7c-2422a08a52e2","added_by":"auto","created_at":"2026-01-16 07:45:44","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75294,"visible":true,"origin":"","legend":"","description":"","filename":"spearman.png","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/f51f5c82247c2248b49e566a.png"},{"id":100008857,"identity":"689b910c-fdb0-47f8-81eb-b87eb81c063d","added_by":"auto","created_at":"2026-01-12 05:57:39","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":34074,"visible":true,"origin":"","legend":"","description":"","filename":"visualization.png","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/6606c06fa9416cb1b29bdf87.png"},{"id":100008860,"identity":"b6562afe-b0b9-4a1f-ab08-86c158a8b55e","added_by":"auto","created_at":"2026-01-12 05:57:39","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125008,"visible":true,"origin":"","legend":"","description":"","filename":"7cf268e6f0df4c4cae801ede1e20b6f01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1/b9cccdd7eb364df900aae7d8.xml"},{"id":109405355,"identity":"1cd47d34-e9fa-4cca-9eba-6e4455ada551","added_by":"auto","created_at":"2026-05-17 13:17:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":715047,"visible":true,"origin":"","legend":"","description":"","filename":"CognitiveDiscourseAnalysiscanbeupscaledusingSentimentAnalysis.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7743985/v1_covered_5b76f14e-80e6-44d6-b122-e561c0f8b0c2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive Discourse Analysis can be up-scaled using Sentiment Analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cognitive discourse analysis, CODA, 038 Sentimentanalysis,SurveyAnalysis,Freetextanaly-039 sis, Natural language processing, VADER, TextBlob","lastPublishedDoi":"10.21203/rs.3.rs-7743985/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7743985/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLanguage is a window into people's thoughts, attitudes, and worldviews, and analyses such as cognitive discourse analysis (CODA) can help reveal this. However, conducting CODA is often resource intensive, requiring considerable time and person-power. By contrast, natural language processing (NLP) approaches may potentially be able to replicate similar results using sentiment analysis for substantially reduced researcher effort. Here, we use respondent-validated data from Amazon product evaluations and Internet Movie Database (IMDB) movie ratings to contrast CODA against two NLP sentiment analysis tools (TextBlob and VADER). NLP approaches showed strong correlations with user-assigned sentiment scores in both data sets. For example, Textblob and VADER show Pearson correlations of \u003cem\u003er\u003c/em\u003e=0.4383 and 0.5188 respectively with the Amazon data and \u003cem\u003er\u003c/em\u003e = 0.594 and \u003cem\u003er\u003c/em\u003e = 0.4664 respectively with the IMDB data (all \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001), while CODA approaches yielded lower correlations (Amazon: \u003cem\u003er\u003c/em\u003e=0.1357; IMDB: \u003cem\u003er\u003c/em\u003e=0.1708 ; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). Our results show that when it comes to sentiment analysis accuracy and dependability, NLP techniques routinely beat conventional CODA methodologies and, therefore, should be widely adopted, enabling CODA to be undertaken at scale. To help this, we provide an open-source widget \\href{https://gitlab.com/lleleena/sasi/-/blob/main/README.md}{Survey Analysis for Sentiment (SASi)}, which reduces the technical requirements of implementing NLP techniques.\u003c/p\u003e","manuscriptTitle":"Cognitive Discourse Analysis can be up-scaled using Sentiment Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 05:57:34","doi":"10.21203/rs.3.rs-7743985/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"00483200-7192-45d7-8f2b-926c7bd4ff66","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-15T14:43:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T19:02:15+00:00","index":56,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60898510,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":60898511,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-05-15T14:55:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 05:57:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7743985","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7743985","identity":"rs-7743985","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}