Titlebook: Computational Linguistics and Intelligent Text Processing; 14th International C Alexander Gelbukh Conference proceedings 2013 Springer-Verl

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An Enhanced Semantic Tree Kernel for Sentiment Polarity Classificationuch applications. This growing demand is supported by an increasing amount and availability of opinionated online information, mainly due to the proliferation and popularity of social media. The majority of work in sentiment analysis considers the polarity of word terms rather than the polarity of s

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Combining Supervised and Unsupervised Polarity Classification for non-English Reviewsining data are provided and the unsupervised methods are usually applied when linguistic resources are available and training data are not provided. Each one of them has its own advantages and disadvantages and for this reason we propose the use of meta-classifiers that combine both of them in order

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Word Polarity Detection Using a Multilingual Approachlysis. In this paper, we propose a multilingual approach for word polarity detection. We construct a word relatedness graph by using the relations in WordNet of a given language. We extend the graph by connecting the WordNets of different languages with the help of the Inter-Lingual-Index based on E

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calorie

Cross-Lingual Projections vs. Corpora Extracted Subjectivity Lexicons for Less-Resourced Languagesusing subjectivity lexicons. However, most of these kinds of resources are often available only for English or other major languages. This work analyses two strategies for building subjectivity lexicons in an automatic way: by projecting existing subjectivity lexicons from English to a new language,

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Distant Supervision for Emotion Classification with Discrete Binary Valuesined by Ekman, we classify emotions according to a set of eight basic . emotions defined by Plutchik (Plutchik’s “wheel of emotions”). This allows us to treat the inherently multi-class problem of emotion classification as a binary problem for four opposing emotion pairs. Our approach applies ., whi

ARM

Using Google n-Grams to Expand Word-Emotion Association Lexicon a target word (a word being considered for inclusion in the lexicon), our proposed approach uses the frequencies, counts or unique words around it within the trigrams from the Google n-gram corpus. The approach was tuned using as training lexicon, a subset of the National Research Council of Canada

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A Joint Prediction Model for Multiple Emotions Analysis in Sentences the single emotion analysis through texts, our work can better reflect people’s inner thoughts by predicting all the possible emotions. We first predict the multiple emotions of words from a CRF model, which avoids the restrictions from traditional emotion lexicons with limited resources and restri

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