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[{"key": "dc.contributor.advisor", "value": "Vihola, Matti", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Lopperi, Mikko", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2023-08-02T09:03:37Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2023-08-02T09:03:37Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2023", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/88486", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "T\u00e4ss\u00e4 opinn\u00e4ytety\u00f6ss\u00e4 tehd\u00e4\u00e4n asiakaspalautteen teemamuutosten tilastollinen monimuuttuja-analyysi keskittyen ensisijaisesti monimuuttujamenetelmiin. Tutkimusaineisto on hankittu Aiwo Digital Oy:lt\u00e4, joka on saanut aineiston asiakasyrityksilt\u00e4\u00e4n. Analyysi keskittyi pseudonymisoituihin teemamuuttujiin, jotka ovat bin\u00e4\u00e4rikoodattuja, ja osoittavat, esiintyik\u00f6 teema yksitt\u00e4isess\u00e4 palautteessa. Teemojen lis\u00e4ksi datassa oli taustamuuttujia ja tunne, joka ilmaisi palautteen s\u00e4vy\u00e4.\n\nEnsisijaisena tavoitteena oli ryhmitell\u00e4 teemat, jotka k\u00e4ytt\u00e4ytyiv\u00e4t samalla tavalla tutkimusjakson aikana. K\u00e4ytimme hierarkkista ryhmittely\u00e4 bin\u00e4\u00e4risen monimuuttujadatan ryhmittelemiseen. Opinn\u00e4ytety\u00f6ss\u00e4 tarkastellaan erilaisia samanlaisuusmittoja bin\u00e4\u00e4risten teemavektoreiden v\u00e4lill\u00e4 ja erilaisuusmittoja ryhmien v\u00e4lill\u00e4. Aukkosuuretta ja siluettisuuretta tarkasteltiin kriteerein\u00e4 optimaalisen ryhm\u00e4m\u00e4\u00e4r\u00e4n valintaan. Ryhmittelimme 79 teemamuuttujaa kahteen ryhm\u00e4\u00e4n. Aggregoimme p\u00e4ivitt\u00e4isen datan viikkotasolle ja tutkimme eri teemaryhmien teemaesiintymi\u00e4. L\u00f6ysimme seitsem\u00e4n teemaa (ryhm\u00e4 1), jotka osoittivat samanlaista k\u00e4ytt\u00e4ytymist\u00e4 koko tutkimusjakson ajan.\n\nK\u00e4sittelimme metrisen moniulotteisen skaalauksen (MDS) teoriaa ja k\u00e4ytimme MDS:\u00e4\u00e4 moniulotteisen teemadatan visualisointiin matalaulotteisessa avaruudessa. Laskimme uusioluottamusv\u00e4lit teemaesiintymille. Tutkimalla luottamusv\u00e4lej\u00e4 havaitsimme, ett\u00e4 kaikki ryhm\u00e4n 1 muutokset eiv\u00e4t n\u00e4ytt\u00e4neet johtuvan ainoastaan satunnaisesta vaihtelusta. K\u00e4ytimme negatiivista binomiregressiota temaesiintymien mallintamiseen viikosta ja tunteesta riippuen. Palaute, jossa ryhm\u00e4n 1 teemoja esiintyi, oli enimm\u00e4kseen negatiivista.\n\nTulosten tulkintaa varten, saimme Aiwolta j\u00e4lkik\u00e4teen tiedot todellisista teemoista, jotka olivat ryhm\u00e4n 1 pseudonymisoitujen teemojen takana. Viisi teemaa ryhm\u00e4ss\u00e4 1 liittyi k\u00e4ytett\u00e4vyyteen ja kaksi asiakaspalveluun. P\u00e4\u00e4ttelimme, ett\u00e4 n\u00e4iden teemojen muutokset saattoivat johtua k\u00e4ytt\u00f6liittym\u00e4n tai joidenkin asiakassovellusten k\u00e4ytt\u00f6tavan muutoksesta. Negatiivinen palaute voi indikoida, miten k\u00e4ytett\u00e4vyyden muutokset on otettu vastaan. On syyt\u00e4 my\u00f6s huomioida, ett\u00e4 merkitt\u00e4v\u00e4 m\u00e4\u00e4r\u00e4 negatiivista palautetta annetaan tyypillisesti silloin, kun jokin ei toimi odotetulla tavalla. Tarkempi tulkinta vaatisi asiakaspalautteiden analysointia tekstitasolla tai asiakasyrityksen omaa arviota.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "This thesis conducts a multivariate statistical analysis of thematic changes in customer feedback, primarily focusing on multivariate methods. The study data were obtained from Aiwo Digital Oy, which received it from their client companies. The analysis focused on pseudonymized binary-coded theme variables, which indicate whether the theme occurred in an individual feedback. In addition to themes, there were also background variables, and sentiment, which indicated the tone of the feedback.\n\nThe primary goal was to group themes that behaved similarly over the study period. We applied hierarchical clustering to group the binary multivariate data. The thesis discusses various similarity measures between binary theme vectors and dissimilarity measures between clusters. The gap statistic and the silhouette coefficient were considered criteria for choosing an appropriate number of clusters. We clustered 79 theme variables into two groups. We aggregated data on a weekly basis and investigated the theme occurrences of different theme groups. Finally, we discovered seven themes (Group 1) that exhibited similar behavior throughout the study period.\n\nWe discussed the theory of metric multidimensional scaling (MDS) and applied metric MDS to visualize the multidimensional theme data in a low-dimensional space. We calculated bootstrap confidence intervals for theme occurrences. Through an investigation of the confidence intervals, we discovered that not all changes in Group 1 appeared to be solely due to natural variation in the data. We applied negative binomial regression to model theme counts depending on the week and the sentiment. Feedback in which themes of Group 1 occurred appeared to be primarily negative.\n\nFor an interpretation of the results, after the study, we were given the real themes behind the pseudonymized themes of Group 1 by Aiwo. Five themes in Group 1 related to usability and two to customer service. We concluded that the changes in these themes were likely due to the change in the user interface or in the method of use of some client applications. The negative feedback may indicate how the changes in usability have been received. Still, it is also worth noting that negative feedback is typically received when something does not function as expected. A thorough analysis of the customer feedback at the text level or the client's assessment would be necessary for a more accurate interpretation.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2023-08-02T09:03:37Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2023-08-02T09:03:37Z (GMT). No. of bitstreams: 0\n Previous issue date: 2023", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "65", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": null, "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "themes of customer feedback", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "binary data", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "hierarchical clustering", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "the gap statistic", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "the silhouette coefficient", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "multidimensional scaling", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "bootstrap confidence intervals", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "negative binomial regression", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Multivariate statistical analysis of thematic changes in customer feedback", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202308024601", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Matemaattis-luonnontieteellinen tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Sciences", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Matematiikan ja tilastotieteen laitos", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Department of Mathematics and Statistics", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tilastotiede", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Statistics", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.collaborator", "value": "business", "language": "", "element": "contractresearch", "qualifier": "collaborator", "schema": "yvv"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "yvv.contractresearch.initiative", "value": "student", "language": "", "element": "contractresearch", "qualifier": "initiative", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.copyright", "value": "\u00a9 The Author(s)", "language": null, "element": "rights", "qualifier": "copyright", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "4043", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "palaute", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "tilastomenetelm\u00e4t", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "monimuuttujamenetelm\u00e4t", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "asiakkaat", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "feedback", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "statistical methods", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "multivariable methods", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "customers", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}]
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