Our experiments test multiple text representations on two binary classification tasks , change of price and polarity.

gested as one of the best methods to summarize into a single value the confusion matrix of a binary classification task (Jurman and Furlanello, 2010; Baldi et al., 2000).

Models are constructed using linear kernel support vector machines for both classification tasks .

Our experiments test several document representations for two binary classification tasks , change of price and polarity.

Bag-of-Words (BOW) document representation is difficult to surpass for many document classification tasks , but cannot capture the degree of semantic similarity among these sentences.

Our work addresses classification tasks that have potential relevance to an influential financial model (Rydberg and Shephard, 2003).

Our two binary classification tasks for news, price change and polarity, are analogous to their activity and direction.

State-of-the-art approaches cast this problem as a classification task and train classifiers using supervised learning (Section 2).

Our approach obviates the need for expensive annotation efforts, and allows us to rapidly bootstrap training data for new classification tasks .

We validate our approach by advancing the state-of-the-art on the most well-studied user classification task : predicting user gender (Section 5).

Table 1: Example instances used for extraction of class attributes for the gender classification task

For the gender classification task , we manually filtered the entire set of attributes to select around 1000 attributes that were judged to be discriminative (two thirds of which are female).

N-gram features are widely used in a variety of classification tasks, therefore we also use them in our polarity classification task .

To conduct our valence prediction study, we used the same human annotators from the polarity classification task for each one of the English, Spanish, Russian and Farsi languages.

We used the same features for the regression task as we have used in the classification task .

The learned lessons from this study are: (l) valence prediction is a much harder task than polarity classification both for human annotation and for the machine learning algorithms; (2) the obtained results showed that despite its difficulty this is still a plausible problem; (3) similarly to the polarity classification task , valence prediction with LIWC is improved when shorter contexts (the metaphor/source/target information source) are considered.

On a three-way classification task between vowels, nasals, and non-nasal consonants, our model yields unsupervised accuracy of 89% across the same set of languages.

To further compare our model to the EM baseline, we show confusion matrices for the three-way classification task in Figure 3.

We further experimented on a three-way classification task involving nasal characters, achieving nearly 90% accuracy.

We consider a semi-supervised classification task , in which the goal is to produce a mapping from an instance space 26 consisting of T-tuples of nonnegative integer-valued features w 2 (ml, .

We evaluate on two text classification tasks : topic classification, and sentiment detection.

However, LR underperforms in classification tasks (in terms of F1, Tables 4-6).