Text Quantification

Abstract

What Is Quantification?In recent years it has been pointed out that, in a number of applications involving classification, the final goal is not determining which class (or classes) individual unlabelled data items belong to, but determining the prevalence (or "relative frequency") of each class in the unlabelled data. The latter task has come to be known as quantification [1, 3, 5-10, 15, 18, 19].Although what we are going to discuss here applies to any type of data, in this tutorial we will mostly be interested in text quantification, i.e., quantification when the data items are textual documents. To see the importance of text quantification, let us examine the task of classifying textual answers returned to open-ended questions in questionnaires [4,11,12], and let us discuss two important such scenarios.In the first scenario, a telecommunications company asks its current customers the question "How satisfied are you with our mobile phone services?", and wants to classify the resulting textual answers according to whether they belong or not to class MayDefectToCompetition. (Membership in this class indicates that the customer is so unhappy with the company's services that she is probably considering a switch to another company.) The company is likely interested in accurately classifying each individual customer, since it may want to call each customer that is assigned the class and offer her improved conditions.In the second scenario, a market research agency asks respondents the question "What do you think of the recent ad campaign for product X?", and wants to classify the resulting textual answers according to whether they belong to the class LovedTheCampaign. Here, the agency is likely not interested in whether a specific individual belongs to the class LovedTheCampaign, but is likely interested in knowing how many respondents belong to it, i.e., in knowing the prevalence of the class.In sum, while in the first scenario classification is the goal, in the second scenario the real goal is quantification. Essentially, quantification is classification evaluated at the aggregate (rather than at the individual) level. Other scenarios in which quantification is the goal may be, e.g., predicting election results by estimating the prevalence of blog posts (or tweets) supporting a given candidate or party [13], or planning the amount of human resources to allocate to different types of issues in a customer support center by estimating the prevalence of M. de Rijke et al.

Erazmo Roterdamiečio leidiniai Vilniaus universiteto bibliotekoje

“…Remtasi Erazmui ir jo aplinkai skirta istoriografija: J. Huizingos parengta Eraz mo biografija, Diarmaido MucColloco išleista krikščionybės istorijos studija 8 , kitais šiai temai nagrinėti skirtais straipsniais 9 ir analogiškais kitų bibliotekų rinkinių tyrimais 10 . Tyrinėjant pasitelkta istoriografija, skirta knygų, leidybos ir bibliotekų istorijai ir konk rečiai VUB rinkiniui 11 apžvelgti.…”

Quantification strategy in social media: Opinion analysis and indicators development in different contexts

“…To understand the indicators behind the avalanche of information, quantification of texts represents a new source for the extraction of sentiments. Sebastiani (2014) defined quantification as the prevalence or "relative frequency" of unlabelled data in textual documents. Different approaches in this area have been analysed in previous works such as real state (Heinig, 2017), psychology (Kumar et al, 2015), and education (Likens et al, 2018), showing examples that quantification strategy is transversal to various disciplines.…”