TITLE:
Hybrid Algorithm to Evaluate E-Business Website Comments
AUTHORS:
Osama M. Rababah, Ahmad K. Hwaitat, Dana A. Al Qudah, Rula Halaseh
KEYWORDS:
Auto-Summarization, Comments Evaluation, Web Search, Semantic-Pragmatic Gap, Natural Language Processing, Machine Learning, Sentiment Detection, Web 2.0
JOURNAL NAME:
Communications and Network,
Vol.8 No.3,
July
27,
2016
ABSTRACT: Online reviews are considered of an important indicator for users to decide on the activity they
wish to do, whether it is watching a movie, going to a restaurant, or buying a product. It also serves
businesses as it keeps tracking user feedback. The sheer volume of online reviews makes it difficult
for a human to process and extract all significant information to make purchasing choices. As
a result, there has been a trend toward systems that can automatically summarize opinions from a
set of reviews. In this paper, we present a hybrid algorithm that combines an auto-summarization
algorithm with a sentiment analysis (SA) algorithm, to offer a personalized user experiences and
to solve the semantic-pragmatic gap. The algorithm consists of six steps that start with the original
text document and generate a summary of that text by choosing the N most relevant sentences in
the text. The tagged texts are then processed and then passed to a Naive Bayesian classifier along
with their tags as training data. The raw data used in this paper belong to the tagged corpus positive
and negative processed movie reviews introduced in [1]. The measures that are used to gauge
the performance of the SA and classification algorithm for all test cases consist of accuracy, recall,
and precision. We describe in details both the aspect of extraction and sentiment detection modules
of our system.