Abstract:
Today the web has changed from static containers of information to dynamic
platforms where users can share digital contents such as blog posts, pictures and
opinions in a very simple manner. Especially, the social media is largely getting
popular due to the fact that most people prefer to share their feelings, thoughts and
memories of their daily activities in social networks. One of the most common types
of posts in social networks is opinion related posts. Moreover, social network users
tend to seek opinions of others before purchasing a product or getting a service.
Social media plays a revolutionary role in travel and tourism industry. With the
increasing use of social media, tourists not only consume tourism products and
services but also prefer to share their experiences with others in the forms of textbased
opinions, comments to other’s posts, pictures with descriptions, ratings, etc.
Current statistics available with Sri Lanka’s tourism authorities do not reveal whether
tourists are happy with the services received during their visit and they have no
information regarding common issues that the tourists have to deal with when they
are in Sri Lanka. However, reading and analyzing all these online posts is not
practically feasible due to the enormous time and human resource that would be
required. The objective of this research is to identify how social media contents
could be used to extract valuable and meaningful information to develop and promote
travel and tourism industry in Sri Lanka.
Our approach is to adopt Sentiment analysis techniques to analyze the text-based
contents shared by tourists on Instagram, which is a popular social networking site
among tourists worldwide, to determine the overall perception of tourists about Sri
Lanka as a travel destination. Photo descriptions and user comments are collected,
using special keywords related to tourism in Sri Lanka using an online tool and, in
the first phase of the research, sentiment classifier with support vector machine
algorithm will be develop to identify sentiment polarity of posts. Furthermore in the
second phase feature analysis model will be developed through which positive posts
with feature words will be used to identify tourists who recommend Sri Lanka to
others or potential tourists who plan to visit/revisit Sri Lanka. Moreover, feature
categorization method will be used to identify the key areas that require
improvements to offer a better service to tourists through negative sentiments.
