In this work, we propose a novel scheme for sentiment classification (without labeled examples) which combines the strengths of both "learn-based" and "lexicon-based" approaches as follows: we first use a lexicon-based technique to label a portion of informative examples from given task (or domain); then learn a new supervised classifier based on these labeled ones; finally apply this classifier to the task. The experimental results indicate that proposed scheme could dramatically outperform "learn-based" and "lexicon-based" techniques.

In this work, we explore the use of error-correcting output codes (ECOC) to enhance the performance of centroid text classifier. The framework is to decompose one multi-class problem into multiple binary problems and then learn the individual binary classification problems by centroid classifier. However, this kind of decomposition incurs considerable bias for centroid classifier, which results in noticeable degradation of performance. To address this issue, we use Model-Refinement to adjust this so-called bias.

With the aim to reduce the dimensionality without sacrificing classification performance, the author gains insights from attribute reduction based on discernibility matrix in rough-set theory and proposes two text feature selection algorithms, i.e., DB1 and DB2. The experimental results indicate that DB2 not only yields much higher accuracy than information gain when the number of features is smaller than 6000, but also incurs much smaller CPU time than information gain.