Yohan Jo

Aspect and sentiment unification model for online review analysis

User-generated reviews on the Web contain sentiments about detailed aspects of products and services. However, most of the reviews are plain text and thus require much effort to obtain information about relevant details. In this paper, we tackle the problem of automatically discovering what aspects are evaluated in reviews and how sentiments for different aspects are expressed. We first propose Sentence-LDA (SLDA), a probabilistic generative model that assumes all words in a single sentence are generated from one aspect. We then extend SLDA to Aspect and Sentiment Unification Model (ASUM), which incorporates aspect and sentiment together to model sentiments toward different aspects. ASUM discovers pairs of {aspect, sentiment} which we call senti-aspects. We applied SLDA and ASUM to reviews of electronic devices and restaurants. The results show that the aspects discovered by SLDA match evaluative details of the reviews, and the senti-aspects found by ASUM capture important aspects that are closely coupled with a sentiment. The results of sentiment classification show that ASUM outperforms other generative models and comes close to supervised classification methods. One important advantage of ASUM is that it does not require any sentiment labels of the reviews, which are often expensive to obtain.

Metaphor Detection with Topic Transition, Emotion and Cognition in Context

Metaphor is a common linguistic tool in communication, making its detection in discourse a crucial task for natural language understanding. One popular approach to this challenge is to capture semantic incohesion between a metaphor and the dominant topic of the surrounding text. While these methods are effective, they tend to overclassify target words as metaphorical when they deviate in meaning from its context. We present a new approach that (1) distinguishes literal and non-literal use of target words by examining sentence-level topic transitions and (2) captures the motivation of speakers to express emotions and abstract concepts metaphorically. Experiments on an online breast cancer discussion forum dataset demonstrate a significant improvement in metaphor detection over the state-of-theart. These experimental results also reveal a tendency toward metaphor usage in personal topics and certain emotional contexts.

Pipeline for expediting learning analytics and student support from data in social learning

An important research problem in learning analytics is to expedite the cycle of data leading to the analysis of student progress and the improvement of student support. For this goal in the context of social learning, we propose a pipeline that includes data infrastructure, learning analytics, and intervention, along with computational models for individual components. Next, we describe an example of applying this pipeline to real data in a case study, whose goal is to investigate the positive effects that goal-setting students have on their peers, which suggests ways in which we might foster these social benefits through intervention.

Metaphor Detection in Discourse

Understanding contextual information is key to detecting metaphors in discourse. Most current work aims at detecting metaphors given a single sentence, thus focusing mostly on local contextual cues within a short text. In this paper, we present a novel approach that explicitly leverages global context of a discourse to detect metaphors. In addition, we show that syntactic information such as dependency structures can help better describe local contextual information, thus improving detection results when combined. We apply our methods on a newly annotated online discussion forum, and show that our approach outperforms the state-of-the-art baselines in previous literature.

Automatic Generation of Issue Analysis Report Based on Social Big Data Mining

In this paper, we propose the system for automatic generation of issue analysis report based on social big data mining, with the purpose of resolving three problems of the previous technologies in a social media analysis and analytic report generation. Three problems are the isolation of analysis, the subjectivity of experts and the closure of information attributable to a high price. The system is comprised of the natural language query analysis, the issue analysis, the social big data analysis, the social big data correlation analysis and the automatic report generation. For the evaluation of report usefulness, we used a Likert scale and made two experts of big data analysis evaluate. The result shows that the quality of report is comparatively useful and reliable. Because of a low price of the report generation, the correlation analysis of social big data and the objectivity of social big data analysis, the proposed system will lead us to the popularization of social big data analysis.

Learning Influence Propagation of Personal Blogs with Content and Network Analyses

Weblogs (blogs) serve as a gateway to a large blog reader population, so blog authors can potentially influence a large reader population by expressing their thoughts and expertise in their blog posts. An important and complex problem, then, is figuring out why and how influence propagates through the blogosphere. While a number of previous research has looked at the network characteristics of blogs to analyze influence propagation through the blogspace, we hypothesize that a blogs influence depends on its contents as well as its network positions. Thus, in this paper, we explore two different influence propagation metrics showing different influence characteristics: Digg score and comment counts. Then, we present the results of our experiments to predict the level of influence propagation of a blog by applying machine learning algorithms to its contents and network positions. We observed over 70,000 blog posts, pruned from over 20,000,000 posts, and we found that the prediction accuracy using the content and the network features simultaneously shows the best F-score in various measures. We expect that this research result will contribute to understanding the problem of influence propagation through the blogosphere, and to developing applications for recommending influential blogs to social web users.