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Item Social media mining for post-disaster management - A case study on Twitter and news(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Banujan, K.; Kumara, B.T.G.S.; Incheon PaikA natural disaster is a natural event which can cause damage to both lives and properties. Social media are capable of sharing information on a real-time basis. Post disaster management can be improved to a great extent if we mine the social media properly. After identifying the need and the possibility of solving that through social media, we chose Twitter to mine and News for validating the Twitter Posts. As a first stage, we fetch the Twitter posts and news posts from Twitter API and News API respectively, using predefined keywords relating to the disaster. Those posts were cleaned and the noise was reduced at the second stage. Then in the third stage, we get the disaster type and geolocation of the posts by using Named Entity Recognizer library API. As a final stage, we compared the Twitter datum with news datum to give the rating for the trueness of each Twitter post. Final integrated results show that the 80% of the Twitter posts obtained the rating of “3” and 15% obtained the rating of “2”. We believe that by using our model we can alert the organizations to do their disaster management activities. Our future development consists mainly of two folds. Firstly, we are planning to integrate the other social media to fetch the data, i.e. Instagram, YouTube, etc. Secondly, we are planning to integrate the weather data into the system in order to improve the precision and accuracy for finding the trueness of the disaster and location.Item Text mining approach on on-line guest reviews: A case study from hospitality industry.(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Hewage, N. I.; Jayalal, S.Social media and consumer-generated context on the Internet have become an integral part of the modern society with millions of users. With the increase of its popularity, tourism industry has shifted towards electronic transactions. Tourists now tend to use online social media reviews and ratings posted by hotel guests to make decisions before booking a hotel, which is an impossible task for a single user due to its high volume of reviews. Hence, to make better decisions, ranking of hotels for a specific region will be beneficial for the tourists who are willing to travel in that region and for the management of the hotel as well. However, while a handful of studies have employed on hotel guest satisfaction and experience by analyzing online hotel guest reviews collected from online travel agencies, there is a significant research problem with ranking hotels by analyzing hotel guest reviews in aspect level consideration. Expedia.com, Agoda.com and Booking.com are some of the leading online travel agencies that have millions of users. Online reviews used for this study are collected from 18 hotels that belong to all these three online travel agencies, and from that dataset, 6 hotels are selected as testing dataset. The dataset contains reviews from year 2010 to 2016. In this study, we propose a ranking mechanism, that ranks hotels by using the overall rating values, sentiment scores and the reviewed year. For computing sentiment scores, each review is split into sentences and they are categorized in to six attributes as Location, Service quality, Cleanliness, Comfort of rooms, Value for money and other. Thereafter, the sentiment analysis is done by considering the weight of the positive and negative words. In this research, we present a novel ranking algorithm to rank hotels, considering the reviewed year and computing the ranking score by getting the variance of the polarity rate and variance of rate of overall rating from initial year to the last. The results were taken by considering specific time period and without considering specific time period. Therefore, when using all the reviews without considering a time period, the rankings deviate from the Booking.com and TripAdvisor.com rankings. When using the reviews within 3 years of time, the ranking results are almost equal to the TripAdvisor.com rankings. When the time period is reduced for 18 months, the accuracy is 50% with Booking.com rankings and 33.3% with TripAdvisor.com rankings. This could be due to the fact that the above mentioned online travel agencies use online reviews more than two years, and therefore it perhaps causes for the deviation of rankings. Since sample dataset is used in this study, the accuracy can be increased by using a large dataset. Since this ranking mechanism considered variance to clarify the performance of hotels, and it is not only depending on the number of positive reviews or star ratings of the hotel, this is beneficial for the hotels which are not much popular, but having good standards.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 Detection of cyber bullying on social media networks(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Priyangika. S.; Jayalal, S.Social Media is becoming an integral part of people’s daily lives today. It is an effective way of sharing one’s life experiences, special occasions, achievements and other events with their friends and family. Although it is a fruitful way to communicate with groups, some people find themselves being insulted or offended by others who are involved in certain post or conversations. These insulations can be based on racism, using profanity or any other vulgar or lewd language. This cyber bullying needs to be monitored and controlled by the social media site owners since it will highly effect on the number and safety of the active site membership. Currently, there is no automated process of identifying offensive comments by the social network site itself. It can be only diagnosed by humans after reading the comments, flagging or reporting them to the owner of the site or blocking the offender. Considering the massive big data set generated in social media daily, automatically detection of offensive statements is required to reduce insulation effectively. For this purpose, text classification approach can be applied where a given text will be categorized as insulting or not, through learning from a pre-learned model. In order to develop the model, data was collected from the popular data repository site named www.kaggle.com. The dataset consists of comments posted on Facebook and Twitter. Firstly the dataset was divided into training data set and test data set. Then the collected data was preprocessed by removing the unwanted strings, correcting words and eliminating duplicate data fields. In the next step, features or keywords were extracted which are qualified to distinguish a statement as ‘insulting’ using N-grams model and counting methods. Feature selection is done using Chi- Squared test and finally apply classification algorithms for separating insulting comments and non-insulting comments from a dataset given. Machine learning algorithms such as Support Vector Machines (SVM), Naïve Bayes, Logistic Regression and Random Forest are used for this. Out of the classification algorithms, SVM is to be performed better than other algorithms since this is a two-class classification problem and a comment is to be classified only into two separate classes which are ‘insulting’ and ‘neutral’. With an exact separation of a given comment into ‘insulting’ and ‘neutral’ category, cyberbullying happening through offensive comments posted on social media sites can be detected.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 Role of social media in shaping communication behavior of urban youth in Bangladesh(Department of Zoology and Environmental Management, University of Kelaniya, Kelaniya, Sri Lanka., 2016) Quarmal, S.B.; Osmani, M.H.It is evident that social media is making strong impact in changing the patterns of interpersonal communication globally. However, the impact is not the same everywhere, especially, it may differ in urban and sub-urban/rural space as well as based on accessibility and quality of Internet. In Bangladesh, few recent phenomena like Shahbag movement have shown that the social media have become a very important communication tool among the urban youth. This paper aims at analyzing what sort of role the social media is playing in shaping their communication patterns and behavior, and how the other factors such as mobility are taking part in such transformation. The study is exploratory, explanatory and descriptive in design, and uses a combination of both quantitative and qualitative methods, namely questionnaire survey and in-depth interview, for data gathering that included 300 students in three cities and towns across the country. To gain more insight out of the survey, academics were interviewed as well. The study reveals that social media has become such an inseparable part in Bangladeshi young people’s lives that they even “feel like crazy” in its absence. The study also reveals that the Dhaka-youths have become more dependent on social media for their day to day communication than the youth living in other two cities. Their interaction with friends, family and relatives is much lesser than that in other two cities. Another interesting finding of the study is that a significant part of the youth barely knows about the means of human communication except social media.