Abstract:In this paper we describe a dynamic normalization process applied to social network multilingual documents (Facebook and Twitter) to improve the performance of the Author profiling task for short texts. After the normalization process, n-grams of characters and n-grams of POS tags are obtained to extract all the possible stylistic information encoded in the documents (emoticons, character flooding, capital letters, references to other users, hyperlinks, hashtags, etc.). Experiments with SVM showed up to 90% of performance.

Commonly, humans have the ability to express their emotions clearly and accurately through oral or written communication. However, a considerable part of the population is limited to express their emotions because they have little mobility in their body, difficulty to speak or deficiencies in their intellectual coefficient. Therefore, this work has two objectives: the first is to implement a methodology to induce emotions of happiness and sadness in people with physical and mental disabilities; the second is to analyze and capture brain activity using a "brain-computer interface (BCI)" to create a corpus of EEG patterns related to the emotions, which can be used for detect their emotional state automatically in future works. This work focuses mainly on present the results obtained in induction emotional states in 46 subjects with cerebral palsy and intellectual disability using film clips, which were evaluated by questionnaire of "Scale for Mood Assessment".

No abstract

No abstract

No abstract