A Review on Quantification Learning <s> 74:13 <s> Quantification is the task of estimating the class-distribution of a data-set. While typically considered as a parameter estimation problem with strict assumptions on the data-set shift, we consider quantification in-the-wild, on two large scale data-sets from marine ecology: a survey of Caribbean coral reefs, and a plankton time series from Martha's Vineyard Coastal Observatory. We investigate several quantification methods from the literature and indicate opportunities for future work. In particular, we show that a deep neural network can be fine-tuned on a very limited amount of data (25 - 100 samples) to outperform alternative methods. <s> BIB001 </s> A Review on Quantification Learning <s> 74:13 <s> Sentiment classification has become a ubiquitous enabling technology in the Twittersphere, since classifying tweets according to the sentiment they convey towards a given entity (be it a product, a person, a political party, or a policy) has many applications in political science, social science, market research, and many others. In this paper, we contend that most previous studies dealing with tweet sentiment classification (TSC) use a suboptimal approach. The reason is that the final goal of most such studies is not estimating the class label (e.g., Positive, Negative, or Neutral) of individual tweets, but estimating the relative frequency (a.k.a. “prevalence”) of the different classes in the dataset. The latter task is called quantification, and recent research has convincingly shown that it should be tackled as a task of its own, using learning algorithms and evaluation measures different from those used for classification. In this paper, we show (by carrying out experiments using two learners, seven quantification-specific algorithms, and 11 TSC datasets) that using quantification-specific algorithms produces substantially better class frequency estimates than a state-of-the-art classification-oriented algorithm routinely used in TSC. We thus argue that researchers interested in tweet sentiment prevalence should switch to quantification-specific (instead of classification-specific) learning algorithms and evaluation measures. <s> BIB002 </s> A Review on Quantification Learning <s> 74:13 <s> In recent years there has been a growing interest in text quantification, a supervised learning task where the goal is to accurately estimate, in an unlabelled set of items, the prevalence (or "relative frequency") of each class c in a predefined set C. Text quantification has several applications, and is a dominant concern in fields such as market research, the social sciences, political science, and epidemiology. In this paper we tackle, for the first time, the problem of ordinal text quantification, defined as the task of performing text quantification when a total order is defined on the set of classes; estimating the prevalence of "five stars" reviews in a set of reviews of a given product, and monitoring this prevalence across time, is an example application. We present OQT, a novel tree-based OQ algorithm, and discuss experimental results obtained on a dataset of tweets classified according to sentiment strength. <s> BIB003
-Relative Absolute Error (RAE) RAE is undefined when any p j = 0. One way to solve it is to smooth both groups of prevalences, p j andp j BIB002 BIB003 : in which the denominator normalizes the set of prevalences. The same expression must be applied for eachp j and then compute Equation (22). BIB002 also introduce a normalized version of RAE: In the context of plankton quantification systems, BIB001 and propose Bray-Curtis dissimilarity (Bray and Curtis 1957). BC dissimilarity is commonly used to analyze abundance data collected at different locations in ecological studies and is defined as The Bray-Curtis dissimilarity is bound between 0 and 1, with 0 meaning that the prediction is perfect. Although the Bray-Curtis dissimilarity metric is able to quantify the difference between samples, it is not a true distance, because it does not satisfy the triangle inequality axiom.