Alice H. Oh

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.

Stochastic language generation for spoken dialogue systems

The two current approaches to language generation, template-based and rule-based (linguistic) NLG, have limitations when applied to spoken dialogue systems, in part because they were developed for text generation. In this paper, we propose a new corpus-based approach to natural language generation, specifically designed for spoken dialogue systems.

Stochastic natural language generation for spoken dialog systems

We describe a corpus-based approach to natural language generation (NLG). The approach has been implemented as a component of a spoken dialog system and a series of evaluations were carried out. Our system uses n-gram language models, which have been found useful in other language technology applications, in a generative mode. It is not yet clear whether the simple n-grams can adequately model human language generation in general, but we show that we can successfully apply this ubiquitous modeling technique to the task of natural language generation for spoken dialog systems. In this paper, we discuss applying corpus-based stochastic language generation at two levels: content selection and sentence planning/realization. At the content selection level, output utterances are modeled by bigrams, and the appropriate attributes are chosen using bigram statistics. In sentence planning and realization, corpus utterances are modeled by n-grams of varying length, and new utterances are generated stochastically. Through this work, we show that a simple statistical model alone can generate appropriate language for a spoken dialog system. The results describe a promising avenue for using a statistical approach in future NLG systems.

Topic chains for understanding a news corpus

The Web is a great resource and archive of news articles for the world. We present a framework, based on probabilistic topic modeling, for uncovering the meaningful structure and trends of important topics and issues hidden within the news archives on the Web. Central in the framework is a topic chain, a temporal organization of similar topics. We experimented with various topic similarity metrics and present our insights on how best to construct topic chains. We discuss how to interpret the topic chains to understand the news corpus by looking at long-term topics, temporary issues, and shifts of focus in the topic chains. We applied our framework to nine months of Korean Web news corpus and present our findings.

Face-Responsive Interfaces: From Direct Manipulation to Perceptive Presence

Systems for tracking faces using computer vision have recently become practical for human-computer interface applications. We are developing prototype systems for face-responsive interaction, exploring three different interface paradigms: direct manipulation, gazemediated agent dialog, and perceptually-driven remote presence. We consider the characteristics of these types of interactions, and assess the performance of our system on each application. We have found that face pose tracking is a potentially accurate means of cursor control and selection, is seen by users as a natural way to guide agent dialog interaction, and can be used to create perceptually-driven presence artefacts which convey real-time awareness of a remote space.

Social Media Dynamics of Global Co-presence During the 2014 FIFA World Cup

Sporting championships and other media events can induce very strong feelings of co-presence that can change communication patterns within large communities. Live tweeting reactions to media events provide high-resolution data with time-stamps to understand these behavioral dynamics. We employ a computational focus group method to identify a population of 790,744 international Twitter users, and we track their behavior before, during, and after the 2014 FIFA World Cup. We pick, in particular, a set of Twitter users who specified the teams that they are supporting, such that we can identify communities of fans of the teams, as well as the entire community of World Cup fans. The structure, dynamics, and content of communication of these communities of users are analyzed to compare behavior outside of the matches to behavior during the event and to examine behavioral responses across languages. Specifically, the temporal patterns of the tweeting volume, topics, retweet- ing, and mentioning behaviors are analyzed. We find there are similarities in the responses to media events, characteristic changes in activity patterns of users, and substantial differences in linguistic features. These findings have implications for designing more resilient socio-technical systems during crises and developing better models of complex social behavior.

Analyzing social media in escalating crisis situations

The rapid diffusion of information and opinions through social media, such as web forums and micro-blogs, is affecting the development of crisis situations, such as the Iranian presidential election, the Egyptian protest, and the ROKS Cheonan sinking. Understanding this rapid widespread diffusion, and assessing what information is spreading, what ideas are becoming common, and who is talking about what, is critical for crisis management. This paper presents a computational system for social media assessing the flow of ideas on the web and changes in who is talking about what. This system, given raw social media data, identifies the key topics, the key paths by which topics evolve, the key individuals who contribute to the topic, and the key influence relations between the contributors. We present this system implemented with the Author-Topic model, the meta-network model, and various computational techniques to find and filter the heavy contributors and influences. We demonstrate the performance of the system, by applying it to social media data surrounding the ROKS Cheonan sinking. We describe the results of assessing the initial and changing perceptions of the event using this system.

Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking. If this party identifies the story as misinformation, it is marked as disputed. However, given the uncertain number of exposures, the high cost of fact checking, and the trade-off between flags and exposures, the above mentioned procedure requires careful reasoning and smart algorithms which, to the best of our knowledge, do not exist to date. In this paper, we first introduce a flexible representation of the above procedure using the framework of marked temporal point processes. Then, we develop a scalable online algorithm, CURB, to select which stories to send for fact checking and when to do so to efficiently reduce the spread of misinformation with provable guarantees. In doing so, we need to solve a novel stochastic optimal control problem for stochastic differential equations with jumps, which is of independent interest. Experiments on two real-world datasets gathered from Twitter and Weibo show that our algorithm may be able to effectively reduce the spread of fake news and misinformation.

A computational analysis of agenda setting

Agenda setting theory explains how media affects its audience. While traditional media studies have done extensive research on agenda setting, there are important limitations in those studies, including using a small set of issues, running costly surveys of public interest, and manually categorizing the articles into positive and negative frames. In this paper, we propose to tackle these limitations with a computational approach and a large dataset of online news. Overall, we demonstrate how to carry out a large-scale computational research of agenda setting with online news data using machine learning.

iLight: information flashlight on objects using handheld projector

Handheld Projectors are novel display devices developed recently. In this paper we present iLight, Information flashLight, which is based on the ongoing research project Guiding Light [9] using a handheld projector. By using a handheld projector with a tiny camera attached on it, system can recognize objects and augment information directly on them. iLight also present a interaction methodology on handheld projector and a novel real-time interactive experiences among users.