There is huge amount of content produced online by amateur authors, covering a large
variety of topics. Sentiment analysis (SA) extracts and aggregates users' sentiments towards …

Image data collected after natural disasters play an important role in the forensics of
structure failures. However, curating and managing large amounts of post-disaster imagery …

Most of the research on text categorization has focused on classifying text documents into a
set of categories with no structural relationships among them (flat classification). However, in …

Most of traditional text categorization approaches utilize term frequency (tf) and inverse
document frequency (idf) for representing importance of words and/or terms in classifying a …

Centroid-based classification is a machine learning approach used in the text classification
domain. The main advantage of centroid-based classifiers is their high performance during …

As the volume of information in digital form increases, the use of Text Categorization
techniques aimed at finding relevant information becomes more necessary. To improve the …

In this paper we study the effect of using unlabeled data in conjunction with a small portion
of labeled data on the accuracy of a centroid-based classifier used to perform single-label …

Current trend in operational text categorization is the designing of fast classification tools.
Several studies on improving accuracy of fast but less accurate classifiers have been …

As a commonly used technique in data preprocessing for machine learning, feature
selection identifies important features and removes irrelevant, redundant or noise features to …

Centroid-based categorization is one of the most popular algorithms in text classification. In
this approach, normalization is an important factor to improve performance of a centroid …