Computer Engineering Commons^™

A Data Driven Approach To Identify Journalistic 5ws From Text Documents, Venkata Krishna Mohan Sunkara

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Textual understanding is the process of automatically extracting accurate high-quality information from text. The amount of textual data available from different sources such as news, blogs and social media is growing exponentially. These data encode significant latent information which if extracted accurately can be valuable in a variety of applications such as medical report analyses, news understanding and societal studies. Natural language processing techniques are often employed to develop customized algorithms to extract such latent information from text.

Journalistic 5Ws refer to the basic information in news articles that describes an event and include where, when, who, what and why …

Automatic Concept Identification Of Software Requirements In Turkish, Fatma Bozyi̇ği̇t, Özlem Aktaş, Deni̇z Kilinç

Turkish Journal of Electrical Engineering and Computer Sciences

Software requirements include description of the features for the target system and express the expectations of users. In the analysis phase, requirements are transformed into easy-to-understand conceptual models that facilitate communication between stakeholders. Although creating conceptual models using requirements is mostly implemented manually by analysts, the number of models that automate this process has increased recently. Most of the models and tools are developed to analyze requirements in English, and there is no study for agglutinative languages such as Turkish or Finnish. In this study, we propose an automatic concept identification model that transforms Turkish requirements into Unified Modeling Language …

A Hybrid Sentiment Analysis Method For Turkish, Buket Erşahi̇n, Özlem Aktaş, Deni̇z Kilinç, Mustafa Erşahi̇n

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a hybrid methodology for Turkish sentiment analysis, which combines the lexicon-based and machine learning (ML)-based approaches. On the lexicon-based side, we use a sentiment dictionary that is extended with a synonyms lexicon. Besides this, we tackle the classification problem with three supervised classifiers, naive Bayes, support vector machines, and J48, on the ML side. Our hybrid methodology combines these two approaches by generating a new lexicon-based value according to our feature generation algorithm and feeds it as one of the features to machine learning classifiers. Despite the linguistic challenges caused by the morphological structure of Turkish, the …