Now showing items 1-6 of 6
Feature selection for microRNA target prediction comparison of one-class feature selection methodologies
Traditionally, machine learning algorithms build classification models from positive and negative examples. Recently, one-class classification (OCC) receives increasing attention in machine learning for problems where the ...
New features for sentiment analysis: Do sentences matter?
(CEUR Workshop Proceedings, 2012)
In this work, we propose and evaluate new features to be used in a word polarity based approach to sentiment classification. In particular, we analyze sentences as the first step before estimating the overall review polarity. ...
Data mining for microrna gene prediction: On the impact of class imbalance and feature number for microrna gene prediction
MicroRNAs (miRNAs) are small, non-coding RNAs which are involved in the posttranscriptional modulation of gene expression. Their short (18-24) single stranded mature sequences are involved in targeting specific genes. It ...
Learning domain-specific polarity lexicons
Sentiment analysis aims to automatically estimate the sentiment in a given text as positive or negative. Polarity lexicons, often used in sentiment analysis, indicate how positive or negative each term in the lexicon is. ...
Adaptation and use of subjectivity lexicons for domain dependent sentiment classification
Sentiment analysis refers to the automatic extraction of sentiments from a natural language text. We study the effect of subjectivity-based features on sentiment classification on two lexicons and also propose new ...
Machine learning based learner modeling for adaptive web-based learning
Especially in the first decade of this century, learner adapted interaction and learner modeling are becoming more important in the area of web-based learning systems. The complicated nature of the problem is a serious ...