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Abstract. Opinion mining deals with determining of the sentiment orientation-positive, negative, or neutral-of a (short) text. Recently, it has attracted great interest both in academia and in industry due to its useful potential applications. One of the most promising applications is analysis of opinions in social networks. In this paper, we examine how classifiers work while doing opinion mining over Spanish Twitter data. We explore how different settings (n-gram size, corpus size, number of sentiment classes, balanced vs. unbalanced corpus, various domains) affect precision of the machine learning algorithms. We experimented with Naïve Bayes, Decision Tree, and Support Vector Machines. We describe also language specific preprocessing-in our case, for Spanish language-of tweets. The paper presents best settings of parameters for practical applications of opinion mining in Spanish Twitter. We also present a novel resource for analysis of emotions in texts: a dictionary marked with probabilities to express one of the six basic emotionsProbability Factor of Affective use (PFA)Spanish Emotion Lexicon that contains 2,036 words.

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Atypical sensory responses are common in autism spectrum disorder (ASD). While evidence suggests impaired auditory-visual integration for verbal information, findings for nonverbal stimuli are inconsistent. We tested for sensory symptoms in children with ASD (using the Adolescent/Adult Sensory Profile) and examined unisensory and bisensory processing with a nonverbal auditory-visual paradigm, for which neurotypical adults show bisensory facilitation. ASD participants reported more atypical sensory symptoms overall, most prominently in the auditory modality. On the experimental task, reduced response times for bisensory compared to unisensory trials were seen in both ASD and control groups, but neither group showed significant race model violation (evidence of intermodal integration). Findings do not support impaired bisensory processing for simple nonverbal stimuli in high-functioning children with ASD.