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The world wide web and more specifically social media are showing tremendous growth over recent years. The connectivity social media provide the world with allows users to more easily share experiences and influence each other through providing sentiment. The large volume of this sentiment calls for automated ways of interpretation to quickly gain insights(More)
One of the main aftereffects of traumatic head injury is slowness of information processing. In the present study, it was hypothesized that an important causal mechanism is a problem in the activation of information stored in memory; this is thought to be due to a reduced redundancy of these representations. An experimental drawing task was employed in(More)
We present the system for automated sentiment analysis on multilingual user generated content from various social media and e-mails. One of the main goals of the system is to make people aware how much positive and negative content they read and write. The output is summarized into a database allowing for basic OLAP style exploration of the data across(More)
We study sentiment analysis beyond the typical granularity of polarity and instead use Plutchik’s wheel of emotions model. We introduce RBEM-Emo as an extension to the Rule-Based Emission Model algorithm to deduce such emotions from human-written messages. We evaluate our approach on two different datasets and compare its performance with the current(More)
The communication presents numerical results in the form of a comparison of predictions due to a numerical algorithm using a previously published exact nonlocal Absorbing Boundary Condition (ABC) and analytical results for the field radiated by an infinitely small dipole. The numerical approximations are derived, and application to the FDTD method is(More)