Author Archives: Kim Schouten


In case you are looking for me on campus, my new office is Tinbergen building H10-21.


Distinguished Paper Award for "Aspect-Based Sentiment Analysis on the Web using Rhetorical Structure Theory"

At the International Conference on Web Engineering 2016, our paper called "Aspect-Based Sentiment Analysis on the Web using Rhetorical Structure Theory" got a Distinguished Paper Award, which is essentially the Best Paper runner-up. I am delighted to receive this award and I thank all my co-authors for their work! See also: http://icwe2016.inf.usi.ch/awards Or download the […]




General Update (incl. new publications)

It has been quite some time since I actually posted something. This is, however, not because there was nothing to tell! I have added three additional publications to the list. Furthermore, I have been able to visit the World Wide Web conference in Seoul, South Korea, which was amazing! Meanwhile, I am doing some research […]




Paper Discussion: Titov & McDonald (2008b)

A Joint Model of Text and Aspect Ratings for Sentiment Summarization - Ivan Titov and Ryan McDonald Characteristics Summary Domain Hotel reviews Sentiment Classes 5-star rating Aspect Detection Method Multi-Aspect Sentiment Model (incorporates MG-LDA model) Performance Summary Aspect Detection Aspect service Average Precision: 75.8% Aspect location Average Precision: 85.5% Aspect rooms (with best # of […]


Paper Discussion: Titov & McDonald (2008a) 1

Modeling Online Reviews with Multi-grain Topic Models - Ivan Titov and Ryan McDonald Characteristics Summary Domain Reviews of products, hotels, and restaurants Sentiment Classes 5-star rating Aspect Detection Method MultiGrain LDA Sentiment Analysis Method PRanking (existing method) Performance Summary Sentiment Analysis Ranking Loss: 0.669 Introduction With the advent of topic models, especially, Latent Dirichlet Allocation […]


Paper Discussion: Choi & Cardie (2008)

Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis - Yejin Choi and Claire Cardie Characteristics Summary Domain MPQA corpus Sentiment Classes Positive / Negative Aspect Detection Method N/A Sentiment Analysis Method Support Vector Machine (MIRA) with compositional inference Sentiment Lexicon Wilson et al. (2005) + General Inquirer Performance Summary Polarity Prediction Accuracy: […]