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    Language identification at word level in Sinhala-English code-mixed social media text
    (IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Shanmugalingam, K.; Sumathipala, S.
    Automatic analyzing and extracting useful information from the noisy social media content are currently getting attention from the research community. It is common to find people easily mixing their native language along with the English language to express their thoughts in social media, using Unicode characters or the Unicode characters written in Roman Scripts. Thus these types of noisy code-mixed text are characterized by a high percentage of spelling mistakes with phonetic typing, wordplay, creative spelling, abbreviations, Meta tags, and so on. Identification of languages at word level become a necessary part for analyzing the noisy content in social media. It would be used as an intimidate language identifier for chatbot application by using the native languages. For this study we used Sinhala-English codemixed text from social media. Natural Language Processing (NLP) and Machine Learning (ML) technologies are used to identify the language tags at the word level. A novel approach proposed for this system implemented is machine learning classifier based on features such as Sinhala Unicode characters written in Roman scripts, dictionaries, and term frequency. Different machine learning classifiers such as Support Vector Machines (SVM), Naive Bayes, Logistic Regression, Random Forest and Decision Trees were used in the evaluation process. Among them, the highest accuracy of 90.5% was obtained when using Random Forest classifier
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    Evaluation of higher education institutions using aspect based sentiment analysis
    (International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Balachandran, L.; Kirupananda, A.
    Demand for formal higher education programs among the younger generation in Sri Lanka, has grown over the past decade. The demand growth has fueled the opening up of many local and internationally affiliated institutes offering a diverse range of degree programs. The selection of the appropriate course from these institutes is challenging given the wide choice. In order to select the appropriate institute, students use the Internet for reviews and user comments, especially from social network sites like Facebook, Twitter and Google plus. This search, involves a cost in terms of time spent for reading the comments and processing whether the standing of the ratings for the program and the institution are appropriate. This task is challenging because of the difficulty to extract sentiment information from a massive set of online reviews. A solution is proposed, using an aspect based sentiment evaluation system that assesses institutions by considering the reviews provided, to overcome this problem. This concept is based on Natural Language Processing (NLP). A web based, automated application tool that retrieves review data from social media networks on the institution and the features of the program, analyzes the sentiment value and provides a rating has been developed.