Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/253
Browse
2 results
Search Results
Conference Object Citation - WoS: 8Citation - Scopus: 11Sentiment Analysis for the Social Media: a Case Study for Turkish General Elections(Assoc Computing Machinery, 2017) Yumusak, Semih; Oztoprak, Kasim; Dogdu, Erdogan; Uysal, ElifThe ideas expressed in social media are not always compliant with natural language rules, and the mood and emotion indicators are mostly highlighted by emoticons and emotion specific keywords. There are language independent emotion keywords (e.g. love, hate, good, bad), besides every language has its own particular emotion specific keywords. These keywords can be used for polarity analysis for a particular sentence. In this study, we first created a Turkish dictionary containing emotion specific keywords. Then, we used this dictionary to detect the polarity of tweets that are collected by querying political keywords right before the Turkish general election in 2015. The tweets were collected based on their relatedness with three main categories: the political leaders, ideologies, and political parties. The polarity of these tweets are analyzed in comparison with the election results.Conference Object Citation - Scopus: 2A Discovery and Analysis Engine for Semantic Web(Assoc Computing Machinery, 2018) Kamilaris, Andreas; Dogdu, Erdogan; Kodaz, Halife; Uysal, Elif; Aras, Riza Emre; Yumusak, SemihThe Semantic Web promotes common data formats and exchange protocols on the web towards better interoperability among systems and machines. Although Semantic Web technologies are being used to semantically annotate data and resources for easier reuse, the ad hoc discovery of these data sources remains an open issue. Popular Semantic Web endpoint repositories such as SPARQLES, Linking Open Data Project (LOD Cloud), and LODStats do not include recently published datasets and are not updated frequently by the publishers. Hence, there is a need for a web-based dynamic search engine that discovers these endpoints and datasets at frequent intervals. To address this need, a novel web meta-crawling method is proposed for discovering Linked Data sources on the Web. We implemented the method in a prototype system named SPARQL Endpoints Discovery (SpEnD). In this paper, we describe the design and implementation of SpEnD, together with an analysis and evaluation of its operation, in comparison to the aforementioned static endpoint repositories in terms of time performance, availability, and size. Findings indicate that SpEnD outperforms existing Linked Data resource discovery methods.