Improved Hierarchical Classifiers for Multi-Way
Sentiment Analysis
Aya Nuseir1,
Mahmoud Al-Ayyoub1, Mohammed Al-Kabi2, Ghasan Kanaan3,
and Riyad Al-Shalabi3
1Jordan University of Science and Technology, Jordan
2Information Technology Department, Al-Buraimi
University College, Oman
3Amman
Arab University, Jordan
Abstract: Sentiment Analysis (SA) is field in computational
linguistics concerned with determining the sentiment conveyed in a piece of
text towards certain entities (such as people, organizations, products,
services, events, etc.) using NLP tools. The considered sentiments can be as
simple as positive vs. negative. A more fine-grained approach known as
Multi-Way Sentiment Analysis (MWSA) is based on ranking systems, such as the
5-star ranking system. In such systems, rankings close to each other can be
confusing; thus, some researchers have suggested that using Hierarchical
Classifiers (HCs) can yield better results compared with traditional Flat
Classifier (FCs). Unlike FCs, which try to address the entire classification
problem at once, HCs employ some kind of tree structures where the nodes are
simple “core” classifiers customized to address a subset of the classification
problem. This study aims to explore extensively the use of HCs to address MWSA
by studying six different hierarchies. We compare these hierarchies using four
well-known core classifiers (SVM, Decision Tree, Naive Bayes, and KNN) using
many measures such as Precision, Recall, F1, Accuracy and Mean Square Error
(MSE). The experiments are conducted on the Large Arabic Book Reviews (LABR) dataset, which consists of 63K book
reviews in Arabic. The results show that using some of the proposed HCs yield
significant improvements in accuracy. Specifically, while the best Accuracy and
MSE for FC are 45.77% and 1.61, respective-ly,
the best accuracy and MSE for an HC are 72.64% and 0.53, respectively. Also,
the results show that, in general, KNN(k-nearest neighbors) benefitted the most
from using hierarchical classification.
Keywords: Sentiment Analysis; Arabic Text
Processing; Hierarchical Classifiers, Multi-Way Sentiment Analysis.
Received March 1, 2017; accepted May 10, 2017