In the web 20 websites such as tripadvisor are fully

a simple average rating to give an overall summary of these reviews.
In the Web 2.0 websites such as Tripadvisor are fully dedicated to storing
hotel reviews so anyone can now search for almost any hotel in any city, and
read user reviews to get an idea on the quality of the hotel. The problem
has now become a problem of reading the most relevant reviews and trying
to get an overall picture of what people who have stayed in this hotel think.
Many sites have started using ranking systems based on relevance (x num-
ber of users have found this relevant) but there is no easy system to get an
overall idea of what all users think. Simply using ratings (usually a value
from 1 to 5) is simply not enough to give us a description of what people
think of a hotel.
The process of reading user reviews when searching for a hotel is a rather
daunting and lengthy task, since there are hundreds of reviews per hotel,
and they tend to vary too much to make a uniform decision. The same
problem affects the hotel owners, in the sense that they also use these re-
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view sites to find out what is wrong with their hotel, and simply reading
the rating a review has given gives you no constructive feedback. In many
sites you now see hotels answering a customers reviews which means they
have to individually read every review and form a conclusion on what that
costumer liked and disliked. This is time consuming and inefficient.
The process of extracting the opinions would eliminate this problem by
summarising reviews in terms of the positive and negative features about a
hotel as expressed by the users opinions. This of course would allow a new
type of customized search were users could give priority to specific features
of a hotel over others, therefore skewing the ratings of a particular hotel.
This is where my opinion mining system comes in, a merge of several
Natural Language Processing, Machine Learning and Information Extrac-
tion techniques aimed at the extraction of user opinions from hotel reviews
in order to provide potential customers with a more intuitive access to the
sentiment expressed in hundreds of reviews.
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1.1 Problem Description
This thesis falls within the feature-based opinion mining type, having as the
basic unit of opinions features of the domain as opposed to larger units used
in many systems such as sentences or documents.
The main focus of this thesis is the development a system for process-
ing a large database of textual hotel reviews in English to extract relevant
opinions from users on a series of predefined features of potential interest
to users. The aim of this system is to replace the baseline which is cur-
rently being used to provide a basic opinion mining service within an online
recommendation service and to improve the systems ability to extract user
opinions in both the accuracy of the opinions being extracted, and the num-
ber of opinions detected. Given that the proposed system aims at being
implemented within a larger framework, it is important to maintain the
same type of input/outputs as the original system as to prevent major mod-
ifications to the online services.