Tapucu, Dilek
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Tapucu, D
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03.04. Department of Computer Engineering
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Former Staff
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13
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117
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5
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10
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1
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6967/5163
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0
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0
WoS Citation Count
45
Scopus Citation Count
97
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0
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0
WoS Citations per Publication
4.50
Scopus Citations per Publication
9.70
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10
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0
| Journal | Count |
|---|---|
| 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012 | 2 |
| 1st International Workshop on Sentiment Discovery from Affective Data, SDAD 2012 | 1 |
| 2008 AAAI Workshop | 1 |
| 36th Annual Computer Software and Applications Conference Workshops, COMPSACW 2012 | 1 |
| Confederated International Workshops on On the Move to Meaningful Internet Systems, OTM 2012 | 1 |
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Conference Object Citation - WoS: 18Citation - Scopus: 25Adaptation and Use of Subjectivity Lexicons for Domain Dependent Sentiment Classification(Institute of Electrical and Electronics Engineers Inc., 2012) Dehkharghani, Rahim; Yanıkoğlu, Berrin; Tapucu, Dilek; Saygın, YücelSentiment analysis refers to the automatic extraction of sentiments from a natural language text. We study the effect of subjectivity-based features on sentiment classification on two lexicons and also propose new subjectivity-based features for sentiment classification. The subjectivity-based features we experiment with are based on the average word polarity and the new features that we propose are based on the occurrence of subjective words in review texts. Experimental results on hotel and movie reviews show an overall accuracy of about 84% and 71% in hotel and movie review domains respectively; improving the baseline using just the average word polarities by about 2% points. © 2012 IEEE.Conference Object Citation - Scopus: 3Ontology Supported Policy Modeling in Opinion Mining Process(Springer Verlag, 2012) Husaini, Mus'ab; Ko, Andrea; Tapucu, Dilek; Saygın, YücelIn e-Society the spreading services offered by Social Web has changed the way of communication and cooperation among citizens, policy-makers, governance bodies and civil society actors. One of the main goals of policymakers is to motivate citizens for participation in policy-making processes. UbiPOL ((Ubiquitous Participation Platform for Policy-making, ICT-2009.7.3(ICT for Governance and Policy Modelling), 2009-2011) aimed to develop a ubiquitous solution, which emphasizes citizens' participation in policy-making processes (PMPs) regardless of their current location and time. Ontology-based opinion mining component of Ubipol system has a crucial role in citizens' commitment, because it empowers them to contribute in policy making. This paper presents the ontology-based semi-automatic approach and tool for sentiment analysis in Ubipol system, which include lexicon extraction from a large corpus of documents. Aspect-based opinion summarization of user reviews and its combination with domain ontology development are discussed as well.Article Citation - WoS: 5Citation - Scopus: 5Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis(Springer Verlag, 2015) Gezici, Gizem; Yanıkoğlu, Berrin; Tapucu, Dilek; Saygın, YücelSentiment analysis aims to automatically estimate the sentiment in a given text as positive, objective or negative, possibly together with the strength of the sentiment. Polarity lexicons that indicate how positive or negative each term is, are often used as the basis of many sentiment analysis approaches. Domain-specific polarity lexicons are expensive and time-consuming to build; hence, researchers often use a general purpose or domain-independent lexicon as the basis of their analysis. In this work, we address two sub-tasks in sentiment analysis. We apply a simple method to adapt a general purpose polarity lexicon to a specific domain [1]. Subsequently, we propose and evaluate new features to be used in a word polarity based approach to sentiment classification. In particular, we analyze sentences as the first step for estimating the overall review polarity. We consider different aspects of sentences, such as length, purity, irrealis content, subjectivity, and position within the opinionated text. This analysis is then used to find sentences that may convey better information about the overall review polarity.We use a subset of hotel reviews from the TripAdvisor database [2] to evaluate the effect of sentence-level features on sentiment classification. Then, we measure the performance of our sentiment analysis engine using the domain-adapted lexicon on a large subset of theTripAdvisor database.Conference Object Citation - WoS: 22Citation - Scopus: 29Learning Domain-Specific Polarity Lexicons(Institute of Electrical and Electronics Engineers Inc., 2012) Demiröz, Gülşen; Yanıkoğlu, Berrin; Tapucu, Dilek; Saygın, YücelSentiment analysis aims to automatically estimate the sentiment in a given text as positive or negative. Polarity lexicons, often used in sentiment analysis, indicate how positive or negative each term in the lexicon is. However, since creating domain-specific polarity lexicons is expensive and time-consuming, researchers often use a general purpose or domain-independent lexicon. In this work, we address the problem of adapting a general purpose polarity lexicon to a specific domain and propose a simple yet effective adaptation algorithm. We experimented with two sets of reviews from the hotel and movie domains and observed that while our adaptation techniques changed the polarity values for only a small set of words, the overall test accuracy increased significantly: 77% to 83% in the hotel dataset and 61% to 66% in the movie dataset. © 2012 IEEE.Conference Object Citation - Scopus: 4An Aspect-Lexicon Creation and Evaluation Tool for Sentiment Analysis Researchers(Springer Verlag, 2012) Husaini, Mus'ab; Koçyiğit, Ahmet; Tapucu, Dilek; Yanıkoğlu, Berrin; Saygın, YücelIn this demo paper, we present SARE, a modular and extendable semi-automatic system that 1) assists researchers in building gold-standard lexicons and evaluating their lexicon extraction algorithms; and 2) provides a general and extendable sentiment analysis environment to help researchers analyze the behavior and errors of a core sentiment analysis engine using a particular lexicon.Conference Object Citation - Scopus: 3An Extension of Ontology Based Databases To Handle Preferences(INSTICC, 2009) Tapucu, Dilek; Ait-Ameur, Yamine; Jean, Stephane; Ünalır, Murat OsmanOntologies have been defined to make explicit the semantics of data. With the emergence of the SemanticWeb, the amount of ontological data (or instances) available has increased. To manage such data, Ontology Based DataBases (OBDBs), that store ontologies and their instance data in the same repository have been proposed. These databases are associated with exploitation languages supporting description, querying, etc. on both ontologies and data. However, usually queries return a big amount of data that may be sorted in order to find the relevant ones. Moreover, in the current, few approaches considering user preferences when querying have been developed. Yet this problem is fundamental for many applications especially in the e-commerce domain. In this paper, we first propose an extension of an existing OBDB, called OntoDB through extension of their ontology model in order to support semantic description of preferences. Secondly, an extension of an ontology based query language, called OntoQL defined on OntoDB for querying ontological data with preferences is presented. Finally, an implementation of the proposed extensions are described.Conference Object Citation - Scopus: 18New Features for Sentiment Analysis: Do Sentences Matter?(CEUR Workshop Proceedings, 2012) Gezici, Gizem; Yanıkoğlu, Berrin; Tapucu, Dilek; Saygın, YücelIn this work, we propose and evaluate new features to be used in a word polarity based approach to sentiment classification. In particular, we analyze sentences as the first step before estimating the overall review polarity. We consider different aspects of sentences, such as length, purity, irrealis content, subjectivity, and position within the opinionated text. This analysis is then used to find sentences that may convey better information about the overall review polarity. The TripAdvisor dataset is used to evaluate the effect of sentence level features on polarity classification. Our initial results indicate a small improvement in classification accuracy when using the newly proposed features. However, the benefit of these features is not limited to improving sentiment classification accuracy since sentence level features can be used for other important tasks such as review summarization.Conference Object Citation - Scopus: 5Performance Comparison of Combined Collaborative Filtering Algorithms for Recommender Systems(Institute of Electrical and Electronics Engineers Inc., 2012) Tapucu, Dilek; Kasap, Seda; Tekbacak, FatihRecommender systems have a goal to make personalized recommendations by using filtering algorithms. Collaborative filtering (CF) is one of the most popular techniques for recommender systems. As usual, huge number of the datasets on the Internet increase the amount of time to work on data. This challenge enforces people to improve better algorithms for processing data with user preferences and recommending the most appropriate item to the users. In this paper, we analyze CF algorithms and present results for combined user-based/item-based CF algorithms for different size of datasets. Our goal is to show combined solution results using Loglikelihood, Spearman, Tanimoto and Pearson algorithms. The contribution is to describe which user based CF algorithms and user/item based combined CF algorithms perform better according to dataset, sparsity, execution time and k-neighborhood values. © 2012 IEEE.Conference Object A Roadmap for Semantifying Recommender Systems Using Preference Management(Springer, 2010) Tapucu, Dilek; Tekbacak, Fatih; Ünalır, Murat Osman; Kasap, SedaThe work developed in this paper presents an innovative solution in the field of recommender systems. Our aim is to create integration architecture for improving recommendation effectiveness that obtains user preferences found implicitly in domain knowledge. This approach is divided into four steps. The first step is based on semantifying domain knowledge. In this step, domain ontology will be analyzed. The second step is to define an innovative hybrid recommendation algorithm based upon collaborative filtering and content filtering. The third step is based on preference modeling approach. And in the fourth step preference model and recommendation algorithm will be integrated. Finally, this work will be realized on Netflix movie data source. © 2011 Springer Science+Business Media B.V.Conference Object Citation - Scopus: 5Metamodeling Approach To Preference Management in the Semantic Web(Association for the Advancement of Artificial Intelligence, 2008) Tapucu, Dilek; Can, Özgü; Bursa, Okan; Ünalır, Murat OsmanPreference is a superiority state to determine the preferable or the superior of one entity, property or constraint to another from a specified selection set. Preference issue is heavily studied in Semantic Web research area. The existing preference management approaches only consider the importance of concepts for capturing users' interests. This paper presents a metamodeling approach to preference management. Preference meta model consists of concepts and semantic relations to represent users' interests. Users may have the same type preferences in different domains. Thus, metamodeling must be used to define similar preferences for interoperability in different domains. In this paper, preference meta model defines a general storage structure to manage different types of preferences for personalized applications. Copyright © 2008, Association for the Advancement of Artificial Intelligence.