71,731 research outputs found
A study on text-score disagreement in online reviews
In this paper, we focus on online reviews and employ artificial intelligence
tools, taken from the cognitive computing field, to help understanding the
relationships between the textual part of the review and the assigned numerical
score. We move from the intuitions that 1) a set of textual reviews expressing
different sentiments may feature the same score (and vice-versa); and 2)
detecting and analyzing the mismatches between the review content and the
actual score may benefit both service providers and consumers, by highlighting
specific factors of satisfaction (and dissatisfaction) in texts.
To prove the intuitions, we adopt sentiment analysis techniques and we
concentrate on hotel reviews, to find polarity mismatches therein. In
particular, we first train a text classifier with a set of annotated hotel
reviews, taken from the Booking website. Then, we analyze a large dataset, with
around 160k hotel reviews collected from Tripadvisor, with the aim of detecting
a polarity mismatch, indicating if the textual content of the review is in
line, or not, with the associated score.
Using well established artificial intelligence techniques and analyzing in
depth the reviews featuring a mismatch between the text polarity and the score,
we find that -on a scale of five stars- those reviews ranked with middle scores
include a mixture of positive and negative aspects.
The approach proposed here, beside acting as a polarity detector, provides an
effective selection of reviews -on an initial very large dataset- that may
allow both consumers and providers to focus directly on the review subset
featuring a text/score disagreement, which conveniently convey to the user a
summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be
published in the Journal of Cognitive Computation, available at Springer via
http://dx.doi.org/10.1007/s12559-017-9496-
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
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