What do you think about this photo? : A novel approach to opinion and sentiment analysis of photo comments

138

What do you think about this photo? A novel

approach to opinion and sentiment analysis of photo comments

Slava Kisilevich* and Christian Rohrdantz

Department of Computer and Information Science, University of Konstanz,

P.O. Box 78, 78457 Konstanz, Germany E-mail: slaks@dbvis.inf.uni-konstanz.de E-mail: christian rohrdantz@uni-konstanz.de

*Corresponding author

Veronica Maidel

School of Information Studies, Syracuse University,

343 Hinds Hall, Syracuse, NY 13244-4100, USA E-mail: vmaidel@syr.edu

Daniel Keim

Department of Computer and Information Science, University of Konstanz,

P.O. Box 78, 78457 Konstanz, Germany E-mail: keim@dbvis.inf.uni-konstanz.de

Abstract: We propose a practical unsupervised approach to opinion and sentiment analysis of photo comments with a real-valued strength orientation.

We extract two types of opinions: opinions that relate to the photo quality and general sentiments targeted towards objects depicted on the photo. Our approach combines linguistic features for part of speech tagging, traditional statistical methods for modelling word importance in the photo comment corpus (in a real-valued scale), and a predefined lexicon for detecting negative and positive opinion orientation. In addition, we apply a semi-automatic photo feature detection method and introduce a set of syntactic patterns to resolve opinion references. The results of our user study among 49 non-expert participants of different ages showed no statistical differences between user evaluation and the algorithm.

Keywords: photo comments; opinion and sentiment analysis; inter-rater agreement; intraclass correlation coefficient; data mining.

Reference to this paper should be made as follows: Kisilevich, S., Rohrdantz, C., Maidel, V. and Keim, D. (2013) ‘What do you think about this photo? A novel approach to opinion and sentiment analysis of photo comments’, Int. J. Data Mining, Modelling and Management, Vol. 5, No. 2, pp.138–157.

Ersch. in: International Journal of Data Mining, Modelling and Management; 5 (2013), 2. - S. 138-157 http://dx.doi.org/10.1504/IJDMMM.2013.053693

Konstanzer Online-Publikations-System (KOPS) URL: http://nbn-resolving.de/urn:nbn:de:bsz:352-248941

139 Biographical notes: Slava Kisilevich received his MSc in Information Systems

Engineering at Ben-Gurion University of the Negev. He currently pursues his PhD in the Information Visualisation and Data Analysis Research Group of Professor Daniel Keim at the University of Konstanz. Christian Rohrdantz received his MSc in Information Engineering at the University of Konstanz, Germany. He currently pursues his PhD in the Information Visualisation and Data Analysis Research Group of Professor Daniel Keim at the University of Konstanz.

Veronica Maidel received her MSc in Information Systems Engineering at Ben- Gurion University of the Negev. She currently pursues her PhD in Information Science and Technology at Syracuse University.

Daniel Keim is Full Professor and Head of the Information Visualisation and Data Analysis Research Group at the University of Konstanz, Germany. He has been actively involved in information visualisation and data analysis research for more than 15 years and developed a number of novel visual analysis techniques for very large datasets with applications to a wide range of application areas including financial analysis, network analysis, geo-spatial analysis, as well as text and multimedia analysis.

This paper is a revised and expanded version of a paper entitled ‘Beautiful picture of an ugly place. Exploring photo collections using opinion and sentiment analysis of user comments’ presented at 2010 International Multiconference on Computer Science and Information Technology (IMCSIT), 18–20 October, Wis³a.

1 Introduction

With the fast development of user-centred internet technologies, we witness a rapid growth of web resources, which not only allow users to obtain, but also to generate their own textual information. This leads to dramatic improvements of products and services.

For example, nowadays it is difficult to imagine that we would book a hotel room without checking the hotel’s overall ranking or without reading comments previously written by other users. We are also less inclined to buy a product without reading comments or ratings about its quality. In fact, written opinions have become essential components in decision-making processes and are common in almost all parts of our life. They are essential parts of blogs, news, financial market reports, product reviews, etc. However, textual information generated on the web grows almost at an uncontrollable pace, and manual skimming through user opinions has become a time consuming process.

A typical task in opinion mining is to determine whether a document (review, comment) is bearing a positive or negative connotation (Hatzivassiloglou and McKeown, 1997; Turney, 2002; Dave et al., 2003; Salvetti et al., 2004; Das and Chen, 2007; Fahrni and Klenner, 2008; Argamon et al., 2007). If either connotation is present, the task can be formulated as a classification problem with two class labels (positive and negative) (Liu, 2009). Three different kinds of approaches have been used: unsupervised (Turney, 2002), semi-supervised (Argamon et al., 2007) and supervised ones (Gamon, 2004;

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