Browsing by Author "Ganegoda, Gamage Upeksha"

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Implementation of a personalized and healthy meal recommender system in aid to achieve user fitness goals
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Lokuge, Chamodi; Ganegoda, Gamage Upeksha
Recent research implies that people’s urge to stay healthy and fit has drastically improved and currently, many people are in need to maintain their physical fitness incorporating healthy food habits into their lives amidst hectic urban lifestyles. Thus, nutrition applications are mushrooming in the fitness domain to aid people to improve their dietary intake, track weight-related elements, and generate meal plans. Considering the applications that are typically built for meal planning, it was apparent that personalized nutrition incorporated with healthy meal suggestions is not well addressed, and hence the need for a personalized meal recommendation system that assists the users to achieve their fitness goals is identified. Learning users’ food preferences and delivering food recommendations that plead to their taste and satisfy nutritional guidelines are challenging. Due to the lack of access to a proper meal planning application or without professional help most users follow ineffective, generic meal plans which hinder them from achieving their fitness goals and often cause long-term and short-term health complications. The proposed implementation aims to bridge the gap between the existing meal planning applications and the potential need for a personalized healthy meal plan. This paper succinctly presents the design and implementation of the proposed personalized and healthy meal recommendation system and further discusses the architecture and the evaluation of the design solution.
Keyword extraction from Tweets using NLP tools for collecting relevant news
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Jayasiriwardene, Thiruni D.; Ganegoda, Gamage Upeksha
Keywords play a major role in representing the gist of a document. Therefore, a lot of Natural Language processing tools have been implemented to identify keywords in both structured and unstructured texts. Text that appears in social media platforms such as twitter is mostly unstructured because of the character limitation. Consequently, a lot of short terms and symbols such as emoticons and URLs are included in tweets. Keyword extraction from grammatically ambiguous text is not easy compared to structured text since it is hard to rely on the linguistic features in unstructured texts. But when it comes to news on twitter, it may contain somewhat structured text than informal text does but it depends on the tweeter, the person who posts the tweet. In this paper, a methodology is proposed to extract keywords from a given tweet to retrieve relevant news that has been posted on twitter, for fake news detection. The intention of extracting keywords is to find more related news efficiently and effectively. For this approach, a corpus that contains tweet texts from different domains is built in order to make this approach more generic instead of making it a domainspecific approach. In fact, the Stanford Core NLP tool kit, Wordnet linguistic database and statistical method are used for extracting keywords from a tweet. For the system evaluation, the Turing test which has human intervention is used. The system was able to acquire an accuracy of 67.6% according to the evaluation conducted.
Source credibility analysis on Twitter users
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Wijesekara, Malith; Ganegoda, Gamage Upeksha
Social media has gained impressive popularity all around the world in the last decade. Social networks such as Twitter, Facebook, LinkedIn, and Instagram have acquired their user’s attraction by maintaining their identity with very similar features. With the popularity of these platforms, now a day most of the users tend to rely on the information published on social media. Therefore, the credibility of social media information is playing a major role in the present cyberspace. As an example, the Twitter platform is handling 500 million tweets per day. Most of the twitter messages are truthful, but the twitter platform is also used to spread rumors and misinformation. Truthfulness or reliability is depending on the source's credibility. Twitter profiles can be identified as the information source on the twitter platform. In this paper, a user reputationbased prediction method is proposed to analyze the twitter source credibility. The proposed solution is mainly based on the k-means clustering model. Another two models namely, news category analysis and sentiment analysis are deployed to generate novel features for the clustering method. The objective of this paper is to introduce a credibility rating method to visualize the user credibility of twitter user profiles. So that followers can have an understanding about the trustworthiness of the information published on that profile. Producing the agreement score for a specific twitter user is one of a novel experiment in this research. Achieved accuracy by the system is 0.68 according to the evaluations conducted.