This study aims to place EFL learners along an affective continuum via machine learning methods and present a new dataset about characteristics of learners. In line with the purposes, written self-reports 475 students from 5 different faculties in 3 universities Turkey were collected manually assigned by researchers one labels (positive, negative, or neutral). As result, two combinations same (...

In this paper we propose a new method of classifying text documents. Unlike conventional vector space models, the proposed method preserves the sequence of term occurrence in a document. The term sequence is effectively preserved with the help of a novel datastructure called ‘Status Matrix’. Further the corresponding classification technique has been proposed for efficient classification of tex...

Nowadays, the automated text classification has witnessed special importance due to the increasing availability of documents in digital form and ensuing need to organize them. Although this problem is in the Information Retrieval (IR) field, the dominant approach is based on machine learning techniques. Approaches based on classifier committees have shown a better performance than the others. I...

Text mining refers to the process of deriving high-quality information from text. Text processing involves in search and replace in electronic format of text. A number of approaches have been developed to represent and classify text documents. Most of the approach tries to attain good classification performance while taking a document only by words. We propose a concept based methodology instea...

This thesis describes research work undertaken in the fields of text and questionnaire mining. More specifically, the research work is directed at the use of text classification techniques for the purpose of summarising the free text part of questionnaires. In this thesis text summarisation is conceived of as a form of text classification in that the classes assigned to text documents can be vi...

Unlabeled documents vastly outnumber labeled documents in text classification. For this reason, semi-supervised learning is well suited to the task. Representing text as a combination of unigrams and bigrams has not shown consistent improvements compared to using unigrams in supervised text classification. Therefore, a natural question is whether this finding extends to semi-supervised learning...

A central problem in information retrieval is the automated classification of text documents. While many existing methods achieve good levels of performance, they generally require levels of computation that prevent them from making sufficiently fast decisions in some applied setting. Using insights gained from examining the way humans make fast decisions when classifying text documents, two ne...

We propose an unsupervised feature generation algorithm using the repositories of human knowledge for effective text categorization. Conventional bag of words (BOW) depends on the presence / absence of keywords to classify the documents. To understand the actual context behind these keywords, we use knowledge concepts / hyperlinks from external knowledge sources through content and structure mi...

The information world is rich of documents in different formats or applications, such as databases, digital libraries, and the Web. Text classification is used for aiding search functionality offered by search engines and information retrieval systems to deal with the large number of documents on the web. Many research papers, conducted within the field of text classification, were applied to E...

In many multilingual text classification problems, the documents in different languages often share the same set of categories. To reduce the labeling cost of training a classification model for each individual language, it is important to transfer the label knowledge gained from one language to another language by conducting cross language classification. In this paper we develop a novel subsp...