Nikos Malandrakis

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In this paper, we present experiments on continuous time, continuous scale affective movie content recognition (emotion tracking). A major obstacle for emotion research has been the lack of appropriately annotated databases, limiting the potential for supervised algorithms. To that end we develop and present a database of movie affect, annotated in(More)
We present an affective text analysis model that can directly estimate and combine affective ratings of multi-word terms, with application to the problem of sentence polarity/semantic orientation detection. Starting from a hierarchical compositional method for generating sentence ratings, we expand the model by adding multi-word terms that can capture(More)
Depression is one of the most common mood disorders. Technology has the potential to assist in screening and treating people with depression by robustly modeling and tracking the complex behavioral cues associated with the disorder (e.g., speech, language, facial expressions, head movement, body language). Similarly, robust affect recognition is another(More)
Emotion recognition algorithms for spoken dialogue applications typically employ lexical models that are trained on labeled in-domain data. In this paper, we propose a domain-independent approach to affective text modeling that is based on the creation of an affective lexicon. Starting from a small set of manually annotated seed words, continuous valence(More)
We estimate the semantic similarity between two sentences using regression models with features: 1) n-gram hit rates (lexical matches) between sentences, 2) lexical semantic similarity between non-matching words, and 3) sentence length. Lexical semantic similarity is computed via co-occurrence counts on a corpus harvested from the web using a modified(More)
Motivated by methods used in language modeling and grammar induction, we propose the use of pragmatic constraints and perplexity as criteria to filter the unlabeled data used to generate the semantic similarity model. We investigate unsupervised adaptation algorithms of the semantic-affective models proposed in [1, 2]. Affective ratings at the utterance(More)
This paper describes our submission to Se-mEval2014 Task 9: Sentiment Analysis in Twitter. Our model is primarily a lexicon based one, augmented by some pre-processing, including detection of Multi-Word Expressions, negation propagation and hashtag expansion and by the use of pairwise semantic similarity at the tweet level. Feature extraction is repeated(More)
This paper describes our submission for Se-mEval2013 Task 2: Sentiment Analysis in Twitter. For the limited data condition we use a lexicon-based model. The model uses an af-fective lexicon automatically generated from a very large corpus of raw web data. Statistics are calculated over the word and bigram af-fective ratings and used as features of a Naive(More)
We investigate acoustic modeling, feature extraction and feature selection for the problem of affective content recognition of generic, non-speech, non-music sounds. We annotate and analyze a database of generic sounds containing a subset of the BBC sound effects library. We use regression models, long-term features and wrapper-based feature selection to(More)
We propose a method of affective text analysis and modeling that is capable of generating continuous valence ratings at the sentence level starting from word and multi-word term valence ratings. Motivated from the language modeling literature, a back-off algorithm is employed to efficiently fuse the valence of single-word and multi-word terms. Specifically,(More)