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User sentiment detection: a YouTube use case
In this paper we propose an unsupervised lexicon-based approach to detect the sentiment polarity of user comments in YouTube. Polarity detection in social media content is challenging not only because of the existing limitations in current sentiment dictionaries but also due to the informal linguistic styles used by users. Present dictionaries fail to capture the sentiments of community-created terms. To address the challenge we adopted a data-driven approach and prepared a social media specific list of terms and phrases expressing user sentiments and opinions. Experimental evaluation shows the combinatorial approach has greater potential. Finally, we discuss many research challenges involving social media sentiment analysis
Automatic domain ontology extraction for context-sensitive opinion mining
Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline
The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps
Third party apps that work on top of personal cloud services such as Google
Drive and Dropbox, require access to the user's data in order to provide some
functionality. Through detailed analysis of a hundred popular Google Drive apps
from Google's Chrome store, we discover that the existing permission model is
quite often misused: around two thirds of analyzed apps are over-privileged,
i.e., they access more data than is needed for them to function. In this work,
we analyze three different permission models that aim to discourage users from
installing over-privileged apps. In experiments with 210 real users, we
discover that the most successful permission model is our novel ensemble method
that we call Far-reaching Insights. Far-reaching Insights inform the users
about the data-driven insights that apps can make about them (e.g., their
topics of interest, collaboration and activity patterns etc.) Thus, they seek
to bridge the gap between what third parties can actually know about users and
users perception of their privacy leakage. The efficacy of Far-reaching
Insights in bridging this gap is demonstrated by our results, as Far-reaching
Insights prove to be, on average, twice as effective as the current model in
discouraging users from installing over-privileged apps. In an effort for
promoting general privacy awareness, we deploy a publicly available privacy
oriented app store that uses Far-reaching Insights. Based on the knowledge
extracted from data of the store's users (over 115 gigabytes of Google Drive
data from 1440 users with 662 installed apps), we also delineate the ecosystem
for third-party cloud apps from the standpoint of developers and cloud
providers. Finally, we present several general recommendations that can guide
other future works in the area of privacy for the cloud
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