Online reviews are often accompanied with numerical ratings provided by users for a set of service or product aspects. We propose a statistical model which is able to discover corresponding topics in text and extract textual evidence from reviews supporting each of these aspect ratings – a fundamental problem in aspect-based sentiment summarization (Hu and Liu, 2004a). Our model achieves high accuracy, without any explicitly labeled data except the user provided opinion ratings. The proposed approach is general and can be used for segmentation in other applications where sequential data is accompanied with correlated signals. 1

In this paper we present a novel framework for extracting the ratable aspects of objects from online user reviews. Extracting such aspects is an important challenge in automatically mining product opinions from the web and in generating opinion-based summaries of user reviews [18, 19, 7, 12, 27, 36, 21]. Our models are based on extensions to standard topic modeling methods such as LDA and PLSA to induce multi-grain topics. We argue that multi-grain models are more appropriate for our task since standard models tend to produce topics that correspond to global properties of objects (e.g., the brand of a product type) rather than the aspects of an object that tend to be rated by a user. The models we present not only extract ratable aspects, but also cluster them into coherent topics, e.g., waitress and bartender are part of the same topic staff for restaurants. This differentiates it from much of the previous work which extracts aspects through term frequency analysis with minimal clustering. We evaluate the multi-grain models both qualitatively and quantitatively to show that they improve significantly upon standard topic models.

Algorithms such as Latent Dirichlet Allocation (LDA) have achieved significant progress in modeling word document relationships. These algorithms assume each word in the document was generated by a hidden topic and explicitly model the word distribution of each topic as well as the prior distribution over topics in the document. Given these parameters, the topics of all words in the same document are assumed to be independent. In this paper, we propose modeling the topics of words in the document as a Markov chain. Specifically, we assume that all words in the same sentence have the same topic, and successive sentences are more likely to have the same topics. Since the topics are hidden, this leads to using the well-known tools of Hidden Markov Models for learning and inference. We show that incorporating this dependency allows us to learn better topics and to disambiguate words that can belong to different topics. Quantitatively, we show that we obtain better perplexity in modeling documents with only a modest increase in learning and inference complexity. 1

Measurements of the impact and history of research literature provide a useful complement to scientific digital library collections. Bibliometric indicators have been extensively studied, mostly in the context of journals. However, journal-based metrics poorly capture topical distinctions in fast-moving fields, and are increasingly problematic with the rise of open-access publishing. Recent developments in latent topic models have produced promising results for automatic sub-field discovery. The fine-grained, faceted topics produced by such models provide a clearer view of the topical divisions of a body of research literature and the interactions between those divisions. We demonstrate the usefulness of topic models in measuring impact by applying a new phrase-based topic discovery model to a collection of 300,000 Computer Science publications, collected by the Rexa automatic citation indexing system.

Email summary keywords, used to concisely represent the gist of an email, can help users manage and prioritize large numbers of messages. We develop an unsupervised learning framework for selecting summary keywords from emails using latent representations of the underlying topics in a user’s mailbox. This approach selects words that describe each message in the context of existing topics rather than simply selecting keywords based on a single message in isolation. We present and compare four methods for selecting summary keywords based on two well-known models for inferring latent topics: latent semantic analysis and latent Dirichlet allocation. The quality of the summary keywords is assessed by generating summaries for emails from twelve users in the Enron corpus. The summary keywords are then used in place of entire messages in two proxy tasks: automated foldering and recipient prediction. We also evaluate the extent to which summary keywords enhance the information already available in a typical email user interface by repeating the same tasks using email subject lines.

This paper proposes a novel topic model, Citation-Author-Topic (CAT) model that addresses a semantic search task we define as expert search – given a research area as a query, it returns names of experts in this area. For example, Michael Collins would be one of the top names retrieved given the query Syntactic Parsing. Our contribution in this paper is two-fold. First, we model the cited author information together with words and paper authors. Such extra contextual information directly models linkage among authors and enhances the author-topic association, thus produces more coherent author-topic distribution. Second, we provide a preliminary solution to the task of expert search when the learning repository contains exclusively research related documents authored by the experts. When compared with a previous proposed model (Johri et al., 2010), the proposed model produces high quality author topic linkage and achieves over 33 % error reduction evaluated by the standard MAP measurement. 1

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Given a movie comment, does it contain a spoiler? A spoiler is a comment that, when disclosed, would ruin a surprise or reveal an important plot detail. We study automatic methods to detect comments and reviews that contain spoilers and apply them to reviews from the IMDB (Internet Movie Database) website. We develop topic models, based on Latent Dirichlet Allocation (LDA), but using linguistic dependency information in place of simple features from bag of words (BOW) representations. Experimental results demonstrate the effectiveness of our technique over four movie-comment datasets of different scales. 1

In this paper, we propose a multiwordenhanced author topic model that clusters authors with similar interests and expertise, and apply it to an information retrieval system that returns a ranked list of authors related to a keyword. For example, we can retrieve Eugene Charniak via search for statistical parsing. The existing works on author topic modeling assume a “bag-of-words ” representation. However, many semantic atomic concepts are represented by multiwords in text documents. This paper presents a pre-computation step as a way to discover these multiwords in the corpus automatically and tags them in the termdocument matrix. The key advantage of this method is that it retains the simplicity and the computational efficiency of the unigram model. In addition to a qualitative evaluation, we evaluate the results by using the topic models as a component in a search engine. We exhibit improved retrieval scores when the documents are represented via sets of latent topics and authors. 1

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