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This open access book provides an introduction and an overview of
learning to quantify (a.k.a. “quantification”), i.e. the task
of training estimators of class proportions in unlabeled data by
means of supervised learning. In data science, learning to quantify
is a task of its own related to classification yet different from
it, since estimating class proportions by simply classifying all
data and counting the labels assigned by the classifier is known to
often return inaccurate (“biased”) class proportion estimates.
The book introduces learning to quantify by looking at the
supervised learning methods that can be used to perform it, at the
evaluation measures and evaluation protocols that should be used
for evaluating the quality of the returned predictions, at the
numerous fields of human activity in which the use of
quantification techniques may provide improved results with respect
to the naive use of classification techniques, and at advanced
topics in quantification research. The book is suitable to
researchers, data scientists, or PhD students, who want to come up
to speed with the state of the art in learning to quantify, but
also to researchers wishing to apply data science technologies to
fields of human activity (e.g., the social sciences, political
science, epidemiology, market research) which focus on aggregate
(“macro”) data rather than on individual (“micro”) data.
Opinion mining is a recent discipline at the crossroads of
Information Retrieval and of Computational Linguistics which is
concerned not with the topic a document is about, but with the
opinion it expresses. It has a rich set of applications, ranging
from tracking users' opinions about products or about political
candidates as expressed in online forums, to customer relationship
management. Functional to the extraction of opinions from text is
the determination of the relevant entities of the language that are
used to express opinions, and their opinion-related properties. For
example, determining that the term beautiful casts a positive
connotation to its subject. In this book we investigate on the
automatic recognition of opinion-related properties of terms. This
results into building opinion-related lexical resources, which can
be used into opinion mining applications.
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