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In this paper, we propose a classifier for predicting message-level sentiments of En-glish micro-blog messages from Twitter. Our method builds upon the convolutional sentence embedding approach proposed by (Sev-eryn and Moschitti, 2015a; Severyn and Mos-chitti, 2015b). We leverage large amounts of data with distant supervision to train an ensemble of(More)
This paper presents a novel approach for multilingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than English is very limited. Previously proposed multilingual approaches typically require to establish a correspondence to English for which powerful classi-fiers are already available.(More)
—The problem of inpainting involves reconstructing the missing areas of an image. Inpainting has many applications , such as reconstructing old damaged photographs or removing obfuscations from images. In this paper we present the directional diffusion algorithm for inpainting. Typical diffusion algorithms are bad at propagating edges from the image into(More)
In this paper we investigate the cross-domain performance of sentiment analysis systems. For this purpose we train a con-volutional neural network (CNN) on data from different domains and evaluate its performance on other domains. Furthermore , we evaluate the usefulness of combining a large amount of different smaller annotated corpora to a large corpus.(More)
In this paper we present SB10k, a new corpus for sentiment analysis with approx. 10,000 German tweets. We use this new corpus and two existing corpora to provide state-of-the-art benchmarks for sentiment analysis in German: we implemented a CNN (based on the winning system of SemEval-2016) and a feature-based SVM and compare their performance on all three(More)
We present <i>RTDS</i>, an Android application to analyze discussions while they are taking place. Using two microphones of a smart phone and Time Difference of Arrival measurements, conversations of participants are evaluated regarding, e.g., speaking time, contributions, or complex interaction patterns. The application can also assume the role of an(More)
English. In this paper, we propose a clas-sifier for predicting sentiments of Italian Twitter messages. This work builds upon a deep learning approach where we leverage large amounts of weakly labelled data to train a 2-layer convolutional neural network. To train our network we apply a form of multi-task training. Our system participated in the(More)
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