Faculty of Computing

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Subject: search.filters.subject.Sentiment Analysis

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Now showing 1 - 7 of 7
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    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, A
    Procrastination 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.
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    PublicationOpen 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, D
    In 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 assistance
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    PublicationOpen 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, A
    Small 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 sales
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    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, K
    Aspect 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.
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    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.
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    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, D
    Sentiment 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.
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    Stock Market Prediction Using Machine Learning Techniques
    (IEEE, 2019-12-05) Sirimevan, N; Mamalgaha, I. G. U. H; Jayasekara, C; Mayuran, Y. S; Jayawardena, C
    Predicting stock market prices is crucial subject at the present economy. Hence, the tendency of researchers towards new opportunities to predict the stock market has been increased. Researchers have found that, historical stock data and Search Engine Queries, social mood from user generated content in sources like Twitter, Web News has a predictive relationship to the future stock prices. Lack of information such as social mood was there in past studies and in this research, we discuss an effective method to analyze multiple information sources to fill the information gap and predict an accurate future value. For this, LSTM - RNN models were employed to analyze sperate sources and Ensembled method with Weighted Average and Differential Evolution technique were used for more accurate prediction of the stock prices. And highly accurate predictions were made to one-day, seven-days, 15-days and 30 days for the future. So that investors could gain an insight into what they are inventing for and the companies to track how well they will perform in the stock market.