Abstract:
Over the last few years, this special task of summarizing opinions has stirred tremendous interest
amongst the NLP and Text Mining communities. The simplest form of an opinion summary is the
result of sentiment prediction. Aspect-based summarization divides input texts into aspects,
which are also called as features and subtopics, and generates summaries of each aspect. Today
millions of web-users express their opinions about many topics through blogs, wikis, web, chats
and social networks. Especially sectors such as e-commerce and e-tourism, it is very useful to
automatically analyze the huge amount of social information available on the Web, but the
extremely unstructured nature of these contents makes it a difficult task. One of the main
reasons for the lack of study on opinions is the fact that there were little opinionated texts
available before Web. Today millions web-users express their opinions about many topics
through blogs, wikis, web, chats and social networks. The objective of this research is to design
and develop aspect opinion summarization for opinionated Amharic documents. The major
components of this work are Amharic opinions/reviews, preprocessing phase, aspect and opinion
learner, opinion word Seed Lexicon, polarity defining and Aspect based Amharic opinion
summarization by graph. The Average performance of user center evaluation for our aspect
based Amharic opinion summarization system for bootstrapping is 91.38% or 4.569 out of 5
weigh. In system centered evaluation the semi supervised bootstrap method achieved the
averages of positive and negative effectiveness 92% precision, 72.8% recall and 81.40% Fmeasure. Also in the naïve Bayes hotel review classification approach average performance test
result is that 75.68% precision, 87.05% recall and 77.86% F-measure less than bootstrapping.
Up to now there is no systems that digesting those huge amount of customer opinions given in
Ethiopic (Amharic language) for understanding opinion holder (customers) need on a given
domain particular entity. Therefore in this work Ethiopic (Amharic language) customer opinions
on a hotel domain was summarize with respect to their aspects /features by graph visualization
with the performance of above. This work will help for organization (such as hotels), individual
(such as hotel users), government intelligence, and business intelligence
