of sentences in the narrative domain of children’s fairy tales, for subsequent usage in appropriate expressive rendering of text-to-speech synthesis. Initial experiments on a preliminary data set of 22 fairy tales show encouraging results over a naïve baseline and BOW approach for classification of emotional

relatively recently, there are a few research works that are devoted to this topic. In this paper, we are focusing on using Twitter, the most popular micro blogging platform, for the task of Emotion analysis. We will show how to automatically collect a corpus for Emotion analysis and opinion mining purposes

Manifold approaches exist for organization of music by genre and/or style. In this paper we propose the use of text categorization techniques to classify artists present on the Internet. In particular, we retrieve and analyze webpages ranked by search engines to describe artists in terms of word

, there are several approaches that try to address this challenge using different Twitter signals (e.g., number of followers, lists, metadata), but results are not clear and sometimes conflicting. In this paper, we propose TRank, a novel method designed to address the problem of identifying the most influential

We present a sentiment classification sys-tem that participated in the SemEval 2014 shared task on sentiment analysis in Twit-ter. Our system expands tokens in a tweet with semantically similar expressions us-ing a large novel distributional thesaurus and calculates the semantic relatedness

-rated online photo sharing Website as data source. We extract certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images. Automated classifiers are built using support vector machines and classification trees. Linear regression on polynomial

It has been recently recognized that heterogeneous information networks composed of multiple types of nodes and links are prevalent in the real world. Both classification and ranking of the nodes (or data objects) in such networks are essential for network analysis. However, so far these approaches

Twitter provides a search interface to its data, along the lines of traditional search engines. But the single ranked list is a poor way to represent the richlystructured Twitter data. A more structured approach that recognizes original messages, re-tweets, people, and documents as interesting

such a mapping, called ranking by pairwise comparison (RPC), first induces a binary preference relation from suitable training data using a natural extension of pairwise classification. A ranking is then derived from the preference relation thus obtained by means of a ranking procedure, whereby different

three different approaches, one unsupervised and two unsuper-vised. They combine machine learning and lexicon-based techniques with an emotional concept model. These approaches were properly adapted to English and Spanish depending on the resources available for each lan-guage. We obtained promising