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Item Instagram sentiment analysis: Discovering tourists’ perception about Sri Lanka as a tourist destination(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Ranaweera, E.H.; Rajapakse, C.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.Item Predicting box office success of movies using sentiment analysis and opinion mining(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Basnayake, H.; Jayalal, S.Movies and social media come together as a result of people sharing their opinions on social media and movie makers using the same platforms for movie promotions. From movie makers to movie goers, many parties are interested in the success or failure of a movie. Forecasting the success of a movie before its release has been a difficult task for many industry analysts. Since film industry’s unpredictable nature, many analysts have come up with different algorithms and mechanisms to predict the success of a movie. One of the mechanisms to predict the box office success is hype analysis. Hype is one of the factors that drive people to the theatres to watch a new movie. Box office opening of a new movie depends on this hype and it will boost up the total box office collection. Hype can be estimated through social media platforms like Twitter. Twitter can be used as a corpus for sentiment analysis and opinion mining. A movie’s success cannot be predicted in a high accurate level solely based on social factors. Classical factors like movie’s brand name, cast, director, etc. are also important aspects in movie’s performance at box office and should be considered as well. However, a highly accurate method for movie box office prediction integrating both social and classical factors is yet to be introduced for this research area. In this study, tweets related to the particular movie before releasing are collected using an archiver tool and are used as input data. Then the collected data is preprocessed in order to get a clean dataset. As a part of sentiment analysis and opinion mining, feature selection is performed using N-gram method in order to filter out irrelevant data records and unlike Bag of words method, this does not require an extensive dictionary of words since it uses combinations of words and letters. Afterwards the data related to classical factors are integrated with the proposed formula in order to predict the opening box office collection of the movie. The proposed formula is an extension of a formula used in a previous research and the new extension represent the inclusion of classical factors. Finally, the results are compared with actual box office data and the previous formula results in order to compare and determine the level of accuracy. Based on initial results, the proposed formula showed of an accuracy level more than 85 percent when the results were compared with actual box office data. Even though it produced a higher accuracy level, the results produced were less than the actual box office values. Thus further testing is needed to determine the actual accuracy level.Item Incident crowdsourcing, tracking and ranking application for environmental problems and issues in Sri Lanka using natural language processing(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Lakshika, M.V.P.T.; Senanayake, S.H.D.Sri Lanka is having numerous critical environmental problems and issues such as deforestation, pollution of water bodies, natural disasters and many other urban problems. Many of the communities who suffer from such environmental problems are not attaining solutions for their vital problems due to lack of awareness, inefficiency and carelessness of amenable parties such as environmental related authorities and ministries in Sri Lanka. Within past few years, virtual communities in Sri Lanka used social media for emphasizing numerous forms of social problems. The intention of this research is to make the awareness of virtual community as a compulsion towards the responsible parties in Sri Lanka which can work as a driving force for stimulating reasonable solutions towards environmental problems. The web based application discussed in this research has been designed to obtain content of such environmental problems by soliciting contributions from crowdsourcing. Online community can report environmental problems by using text and images. Users of the application can vote and comment on the problems and issues posted in the application. Each problem will receive points based on up or down votes and comments they have received and then the application ranks genuine high quality environmental problems while allocating points for each user in the system. Text categorization is a subtask of information retrieval which used in this application is very effective for filtering of environmental related information before posting to the system. Further, this application is using the semantic information or the polarity of user comments as positive, negative or neutral which are not used yet for the most important natural language applications. This research discussed about a study of the interaction between Natural Language Processing and text categorization. Based on users negative or positive comments and up or down votes, the application calculates the points for each post according to a predefined criterion and highlights the genuine high quality environmental problems and issues in Sri Lanka.