Research Publications
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Publication Embargo Using Sentiment Analysis to Explore the Accommodation Experience in the Sharing Economy through Topic Modeling(IEEE, 2022-12-09) Bandara, H.M.A.P.; Charles, J.; Lekamge, L. S.The rapid proliferation of internet-based technology has made the sharing economy the next e-commerce business model. Recently, sharing economy lodging platforms have gained a significant market share in the tourism and lodging industry. Tourism and hospitality industries are now being significantly disrupted by Airbnb, an online lodging platform. For businesses and customers who utilize these accommodation platforms, online reviews serve as quality indicators, affecting their decisions to make a transaction. Sentiment analysis and text mining can be used to analyze these online reviews to identify various factors embedded in them that can influence how guests perceive lodging in the sharing economy. Peer-to-peer accommodation platforms can benefit from analyzing these aspects since they can utilize the results to streamline their operations and give customers better services. Current research on this domain has only identified a limited number of important factors, such as trust, quality, security, price, cleanliness, and indoor environmental quality. However, there can be many other factors that can affect the accommodation experience. These factors would require further attention. Therefore, in this study a dataset pertaining to the Airbnb platform was considered which contained a total of 401 964 review comments. Word cloud, frequency distribution, and topic modeling were used as data analysis techniques to identify various factors affecting accommodation experience. Results indicate that factors including location, safety, host-guest interaction, amenities, proximity to restaurants and transit options, and apartment uniqueness can be primarily taken into account to give superior services to their clients.Publication Embargo PRODEP: Smart Social Media Procrastination and Depression Tracker(Institute of Electrical and Electronics Engineers, 2022-11-04) Kulatilake, T.T; Liyanage, P.L.R.S.; Deemud, G.H.K.; De Silva, U.S.C; Sriyaratna, D; Kugathasan, AProcrastination refers to the voluntary delay of urgent tasks and can have several negative consequences such as stress, health issues and academic underachievement [47]. It is viewed within physiological research as a self-regulation failure [48]. Similar to procrastination, another severe problem which comes up within lots of people including students and teenagers is "Depression". Depression is a massively widespread problem among people around the world as well as in Sri Lanka [49]. As a result of procrastination and depression, students has to face academic underachievement. One of the main cause of these widespread problems are Social media over-usage [50]. Therefore this paper presents a new tracker which presented as a mobile application with four main components. This research study is about identifying and tracking users' facial emotions and eye-aspect ratio to analyze real emotions of the user via device inbuilt webcam to identify user fatigueness and procrastination. This study also analyzes user behavior in two selected social media platforms which are Facebook and Twitter and identifies the negativity and depressiveness of "Sinhala"content using Machine learning based Sentiment analysis approaches. Also as a companion, this paper introduces a chat-bot which communicates with the user in "Singlish"language. Our final products will be a complete mobile application which generates reports to the user based on the analysis done in the four components. As future work we will introduce AutoML approaches instead of traditional machine learning based approaches.Publication Embargo Intelligent System for Skin Disease Detection of Dogs with Ontology Based Clinical Information Extraction(Institute of Electrical and Electronics Engineers Inc., 2022-10-29) Rathnayaka, R. M. N. A; Anuththara, K. G. S. N; Wickramasinghe, R.J.P; Gimhana, P. S; Weerasinghe, L; Wimalaratne, GThe largest organ in dogs, the epidermis, is crucial in supplying immunological responses. Skin will preserve all the nutrients and safeguard the cells while warding off harmful or pathogenic substances. Most dog owners today are not aware that their pet dog has a skin condition. Although they were aware of these ailments, they had no notion of how to cure them. In such a situation, the dog may experience pain and an aggravation of the condition. Owners should therefore take their dogs to the vet, even if the skin condition is minor. It can, however, be a costly procedure. There aren't many forums where dog owners may get advice from professionals and ask inquiries regarding their pets. The solution suggests a fully functional mobile application which is a combination of disease identification feature, disease severity level detection feature, domain specific knowledge base with semantic web development and a domain specific AI based chat-bot to the dog owners to overcome this problem using Convolutional Neural Network (CNN) and natural language processing (NLP).System will extract the necessary features from the images of the lesion to classify the skin condition and Severity level of the disease. The results obtained show disease type classification is within the accuracy range of 77.78% to 100% which tested again 4 CNN base models. As for the severity level identification accuracy situated around 99.62%.Publication Open Access Automated Customer Care Service System for Finance Companies(NCTM, 2014-12-16) Warnapura, A. K; Rajapaksha, D. S; Ranawaka, H. P; Fernando, P. S. S. J; Kasthuriarachchi, K. T. S; Wijendra, DIn general, to obtain information about a product one should visit the company or contact the company via a phone call or some sort of a communication type, for example E-mail. Even so under normal circumstances the customer will receive the necessary information sent by a human being. There can be many disadvantages in this method. At the onset if a particular customer gives a phone call to the company the customer will have to wait for a considerable time. This is obvious because due to lack of human resources and phone lines there may be a question of customers waiting to get connected to the company line. On the other hand if a customer sends an email, the reply for the email will take time because the particular email should be perused by another human being at company in order to reply. These are few disadvantages apart from human errors that can happen. Ultimately as a result of above detrimental facts a faithful customer could get unsatisfied and lose confidence on a particular company. However, in the system that we are going to introduce, a particular customer can get any type of information in real time by the Aid of the Artificial Intelligence in the form of text/voice or E-mails. The advantages over the other method are that the customers will not have to wait for a reply, there are no space for human error and more importantly the company can use their human resources in other activities while the system takes care of the Customer care unit at least partially. Further, this system will be help to people who needs the immediate customer care assistance and will be able to get help by their own without involved human agent in another party for their assistancePublication Open Access Mini Market: Information Technology Based Support Tool for Small and Medium Scale Enterprises in Sri Lanka(ICRD Publicatio, 2019-07) Thilakarathne, S; Herath, S; Rajapaksha, A; Karunasena, ASmall and medium enterprises (SMEs) play a crucial role in developing countries such as Sri Lanka in growth of an economy. Recently online platforms are being extensively used by SMEs for both marketing and selling items. In a context of keen competition among the online selling platforms, sellers are increasingly feeling the pressure for improving their sales and marketing strategies. When investigating existing problems of SMEs, we were able to find they do not have proper guidance to improve their own business. Simply, the SMEs cannot identify their own marketing level among the other competitors, they haven't any suitable guidelines to identify how they can improve their own market and they have to use manual reports to get their own sales details for visualizing their marketing level where they waste their valuable time and money for visualizing sales market outcomes. In consideration of this, we propose a web system, that examines the effects of three categories in this system, i.e. Seller trustworthiness, analyze customer's emotions, feelings, thoughts, and opinions through Social media (Facebook) and sales prediction component. This system facilitates a multiple seller platform, where they can dynamically manage virtual shop inside this platform. It increases their stability and it will provide directions to overcome economic and unemployment barriers in our country. The results support our research hypotheses partially. The findings of this study are expected to provide some suggestions for sellers on promote and improve of their salesPublication Embargo Supervised learning based approach to aspect based sentiment analysis(IEEE, 2016-12-08) Pannala, N. U; Nawarathna, C. P; Jayakody, J. T. K; Rupasinghe, L; Krishnadeva, KAspect base sentiment analysis is a very popular concept in the machine learning era which is under the research domain still at the movement. This research mainly consist of the way of exploring the sentiment analysis based on the trained data set to provide the positive, negative and neutral reviews for different products in the marketing world. Most of the existing approaches for opinion mining are based on word level analysis of texts and are able to detect only explicitly expressed opinions. In aspect-based sentiment analysis (ABSA) the aim is to identify the aspects of entities and the sentiment expressed for each aspect. The ultimate goal is to be able to generate summaries listing all the aspects and their overall polarity. For this research mainly natural language and machine learning techniques are used. To train the application for the given data sets SVM (support vector machine) and ME (Maximum Entropy) classification algorithms have been used. Differentiation of the performance of the each algorithm will be analyzed through this research using the proven technologies available in the world like "Re call", "F-Measure" and Accuracy.Publication Embargo Computational Model for Rating Mobile Applications based on Feature Extraction(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gunaratnam, I.; Wickramarachchi, D.N.Google Play Store and App Store allow users to share their opinions and helps to measure users satisfaction level about the app through user comments. However, it's highly time-consuming to process all reviews manually. The usefulness of star ratings is limited for development teams since a rating represents an average of both positive and negative evaluations. Therefore, an automated solution is needed to systematically analyze reviews and other textual forms of data. The main objective of this research is to build a platform that rate apps by feature extraction and sentiment analysis to calculate the functionality index of apps based on metrics obtained by surveying 204 mobile phone users. The 5 topmost metrics obtained from them among the 16 metrics obtained from the literature review are usability, price, and frequency of updates, ad-freeness and battery consuming level. This research focuses on selected apps in music and audio category. To perform app rating indexes calculation of the overall app's reviews; data extraction, data cleaning, POS tagging, feature extraction, feature/feature values pairing, weighted feature rating, overall apps' rating and feature-wise app rating is done on textual data. The accuracy of the created model is measured by the level of satisfaction from users.Publication Embargo SmartCoach: Comprehensive Tutor Recommender and Student Attentiveness Measuring Platform(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Samarasekara, H. D. K.; Hansaka, H. M. P.; Rajapaksha, S. D. D.; Tharaka, W. A. D. G.; Manathunga, K.; Sriyarathna, D.C.With the pandemic, coaching or tutoring classes became to halt as the underlying system wasn’t facilitating to adapt to emergency teaching mechanisms due to sparse of relevant technologies and tutor unawareness. SmartCoach is an integrated platform that allows students to find suitable tutors based on recommendations and other parameters like proximity, previous track record etc. using sentiment analysis and natural language processing. For tutors, SmartCoach allows publishing learning materials, automatic quiz creation, tracking attendance and, attentiveness during classes using OpenCV library. Moreover tutors get a dashboard featuring critical aspects about their classes and, personal income. This research is aimed to introduce a comprehensive distance learning platform with novel technological approaches that connect both potential students and tutors to a common platform.Publication Embargo JESSY: An Intelligence Travel Assistant(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Dilshan, K.K.D.N.; Parussella, U.M.D.M.; Herath, H.M.C.J.; Chandranath, C.A.J.P.; Thelijjagoda, S.; Jayalath, T.Sri Lanka has a lengthy history as a tourist destination, but due to political and social forces, there have been numerous ups and downs. In Sri Lanka, the tourism industry is regarded as the third most important economic activity. As per the other countries in the world, Sri Lanka also facing the Covid19 pandemic situation, and the research team has identified several issues that need to be addressed immediately. When tourists visit Sri Lanka, some of them utilize package-based services provided by travel companies, whereas backpackers are independent budget travelers who book hotels, modes of transportation, meal plans, and destinations independently. Thus, ‘JESSY’, the intelligent travel assistant has been proposed with the intension to help independent travelers to travel around the country safely. Accordingly, the ‘JESSY’ is a collection of all the items that a traveler should carry with them every times. The main objective of the research team was to develop a mobile application which includes a leisure time planner, a trustworthy travel guide recommendation for booking, a virtual guide experience for travelers, a chatbot that offers automatic replies by analyzing and assessing data and information, utilities, resources, and safety. 'JESSY' is available for both Android and IOS operating systems.Publication Embargo Ontological Approach for Aspect-Based Sentiment Analysis(IEEE, 2019-12-06) Silva, A. R. S; Rathnayaka, R. R. M. U. A; Wijeratne, P. M. A. K; Deshani, M. T. N; Priyadarshana, Y. H. P. P; Kasthurirathna, DSentiment analysis is used to quantify the sentiment of unstructured text, which could be used to draw insights on public opinions about products, services, or any topic that is of relevance. Most of the available sentiment analysis implementations provide an overall sentiment which is not sufficient because it does not provide an aspect-based sentiment. In this work, we present an extendible ontology-based approach for aspect-based sentiment analysis. In this work, we propose a novel and extendible Ontological approach for aspect-based Sentiment Analysis. It also focuses on learning the aspects using topic modelling to automate the aspect detection. The results indicate that the proposed approach can be effective in classifying sentiments under different aspects. The proposed approach may have applications in domains such as e-commerce web sites, product reviewing platforms and social media platforms.