Predicting market segmentation variables using Twitter following relations

dc.contributor.advisorAlatrista Salas, Hugo
dc.contributor.authorBrossard Núñez, Ian Paules_ES
dc.date.accessioned2018-12-03T20:42:03Zes_ES
dc.date.available2018-12-03T20:42:03Zes_ES
dc.date.created2018es_ES
dc.date.issued2018-12-03es_ES
dc.description.abstractFrom the beginning, social sciences have looked to categorize people into groups that share common characteristics, to better serve the population, giving a distinguished treatment to each group. Applying this approach to the planning of business activities, we can better understand people’s needs, choosing the most favorable marketing strategies for each stratum of customers (saving effort in advertising and distribution) and maximize the level of satisfaction of each of market segment. Social Media is not a stranger to this principle: a correct segmentation will allow companies to avoid bringing content to people that are not part of their target audience, and to better respond to comments and complaints about their products and brands. However, some Social Media like Twitter still haven’t included demographic markers about their users within their marketing platforms, rendering decision-making difficult. In this paper, we demonstrate that it is possible to estimate important demographic information in Social Media by analyzing the tastes and preferences of the users (represented through the Twitter accounts they follow). We present four predictive models that allowed us to estimate the gender, age, socio-economic level and LATIR Lifestyle of a Twitter user. These models were trained using machine learning algorithmses_ES
dc.description.uriTrabajo de investigaciónes_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12404/13072
dc.language.isoenges_ES
dc.publisherPontificia Universidad Católica del Perúes_ES
dc.publisher.countryPEes_ES
dc.rightsinfo:eu-repo/semantics/closedAccesses_ES
dc.subjectTwitteres_ES
dc.subjectAprendizaje automático (Inteligencia artificial)es_ES
dc.subjectSegmentación del mercadoes_ES
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.00es_ES
dc.titlePredicting market segmentation variables using Twitter following relationses_ES
dc.typeinfo:eu-repo/semantics/masterThesises_ES
renati.advisor.dni23976103
renati.discipline611087es_ES
renati.levelhttps://purl.org/pe-repo/renati/level#maestroes_ES
renati.typehttp://purl.org/pe-repo/renati/type#trabajoDeInvestigaciones_ES
thesis.degree.disciplineInformática con mención en Ciencias de la Computaciónes_ES
thesis.degree.grantorPontificia Universidad Católica del Perú. Escuela de Posgradoes_ES
thesis.degree.levelMaestríaes_ES
thesis.degree.nameMaestro en Informática con mención en Ciencias de la Computaciónes_ES

Files

Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
BROSSARD_NÚÑEZ_IAN_PAUL_PREDICTING_MARKET.pdf
Size:
878.98 KB
Format:
Adobe Portable Document Format
Description:

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: