Resolving Name Conflicts for Mobile Apps in Twitter Posts
Résumé
The Twitter platform has emerged as a leading medium of conducting
social commentary, where users remark upon all kinds of entities,
events and occurrences. As a result, organizations are starting to
mine twitter posts to unearth the knowledge encoded in such
commentary. Mobile applications, commonly known as mobile apps, are the fastest growing
consumer product segment in the history of human merchandizing, with
over 600,000 apps on the Apple platform and over 350,000 on Android.
A particularly interesting issue is to evaluate the popularity of
specific mobile apps by analyzing the social conversation on them.
Clearly, twitter posts related to apps are an important segment of
this conversation and have been a main area of research for us. In
this respect, one particularly important problem arises due to a
name conflict of mobile app names and the names that are used to
refer the mobile apps in twitter posts. In this paper, we present a
strategy to reliably extract twitter posts that are related to
specific apps, but discovering the contextual clues that enable
effective filtering of irrelevant twitter posts is our concern.
While our application is in the important space of mobile apps, our
techniques are completely general and may be applied to any entity
class. We have evaluated our approach against a popular Bayesian
classifier and a commercial solution. We have demonstrated that our
approach is significantly more accurate than both of these. These
results as well as other theoretical and practical implications are
discussed.
Origine : Fichiers produits par l'(les) auteur(s)
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