Our experiments consist of an opinion retrieval task and a sentiment classification task .

Since MOAT is a classification task , we use a threshold parameter to draw a boundary between opinionated and non-opinionated sentences.

4.3 Classification task — SVM

(2002) presents empirical results indicating that using term presence over term frequency is more effective in a data-driven sentiment classification task .

The SVM learning algorithm as implemented in the LIB SVM software package (Chang and Lin, 2001) is used for classifier training, owing to its robust performance on many text classification tasks .

We recast it as a multi-class, multi-label text classification task , and presented a bootstrapping algorithm for improving the prediction of minority classes in the presence of a small training set.

Unlike newswire articles, at which many topic-based text classification tasks are targeted, the ASRS reports are informally written using various domain-specific abbreviations and acronyms, tend to contain poor grammar, and have capitalization information removed, as illustrated in the following sentence taken from one of the reports.

The difficulty of a text classification task depends on various factors, but typically, the task can be difficult if (1) the amount of labeled data available for learning the task is small; (2) it involves multiple classes; (3) it involves multi-label categorization, where more than one label can be assigned to each document; (4) the class distributions are skewed, with some categories significantly outnumbering the others; and (5) the documents belong to the same domain (e. g., movie review classification).

Hence, cause identification can be naturally recast as a text classification task : given an incident report, determine which of a set of 14 shapers contributed to the occurrence of the incident described in the report.

Since we recast cause identification as a text classification task and proposed a bootstrapping approach that targets at improving minority class prediction, the work most related to ours involves one or both of these topics.