This paper studies the topic modeling problem of tagged documents and images. Higher-order relations among tagged documents and images are major and ubiquitous characteristics, and play positive roles in extracting reliable and interpretable topics. In this paper, we propose the tag-topic models (TTM) to depict such higher-order topic structural dependencies within the Markov random field (MRF) framework. First, we use the novel factor graph representation of latent Dirichlet allocation (LDA)-based topic models from the MRF perspective, and present an efficient loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Second, we propose the factor hypergraph representation of TTM, and focus on both pairwise and higher-order relation modeling among tagged documents and images. Efficient loopy BP algorithm is developed to learn TTM, which encourages the topic labeling smoothness among tagged documents and images. Extensive experimental results confirm the incorporation of higher-order relations to be effective in enhancing the overall topic modeling performance, when compared with current state-of-the-art topic models, in many text and image mining tasks of broad interests such as word and link prediction, document classification, and tag recommendation.

This paper focuses on performing Sentiment Analysis of Twitter by adapting data from other domains, commonly referred to as Domain Adaptation. While we show that Domain Adaptation is useful in predicting sentiments, we propose different techniques to select an out-of-domain data source that would aid in Sentiment Analysis. Additionally, we suggest two iterative algorithms based on Expectation-Maximization (EM) and Rocchio SVM that filter noisy data during adaptation and train only on valid data. Finally, we explore a couple of metrics, Mutual Information and Cosine distance to measure similarity between different domains of data. We use Twitter and Blippr as data sources and perform binary sentiment (positive and negative sentiments) classification.

This paper reports on a work in progress whose goal is to use Latent Dirichlet Allocation (LDA) to discover topic clusters within a small set of Twitter posts. Preliminary results indicate that micro-documents are amenable to topic clustering via LDA provided that a) only nouns and verbs are used; b) posts are “padded” with words of similar meaning to those used in the posts. These preliminary findings are consistent with the fact that probabilistic topic models look for word co-occurrences in documents and hence require that topic-indicative words appear together many times throughout the data sample. The results of this pilot study are to be extended to a larger data set of Twitter posts to see whether padding” can counteract the growing size of the data and the presence of numerous information-sparse posts.

While microblogging has gained popularity on the Internet, analyzing and processing short messages has become a challenging task in natural language processing. This paper analyzes the differences between Internet short messages (or “microtext”) and general articles by comparing the Plurk Corpus and the Sinica Balanced Corpus. Likelihood ratio and the tóngyìcícílín thesaurus are adopted to analyze the lexical semantics of frequent terms in each corpus. Furthermore, the NTUSD sentiment dictionary is used to compare the sentiment distribution of the two corpora. The result is also applied to sentiment transition analysis.

The Stock Sonar (TSS) is a stock sentiment analysis application based on a novel hybrid approach. While previous work focused on document level sentiment classification, or extracted only generic sentiment at the phrase level, TSS integrates sentiment dictionaries, phrase-level compositional patterns, and predicate-level semantic events. TSS generates precise in text sentiment tagging as well as sentiment-oriented event summaries for a given stock, which are also aggregated into sentiment scores. Hence, TSS allows investors to get the essence of thousands of articles every day and may help them to make timely, informed trading decisions. The extracted sentiment is also shown to improve the accuracy of an existing document-level sentiment classifier.

Much of opinion mining research focuses on product reviews because reviews are opinion-rich and contain little irrelevant information. However, this cannot be said about online discussions and comments. In such postings, the discussions can get highly emotional and heated with many emotional statements, and even personal attacks. As a result, many of the postings and sentences do not express positive or negative opinions about the topic being discussed. To find people’s opinions on a topic and its different aspects, which we call evaluative opinions, those irrelevant sentences should be removed. The goal of this research is thus to identify evaluative opinion sentences. A novel unsupervised approach is proposed to solve the problem, and our experimental results show that it performs well.

Sentiment analysis is the task of determining the attitude (positive or negative) of documents. While the polarity of words in the documents is informative for this task, polarity of some words cannot be determined without domain knowledge. Detecting word polarity thus poses a challenge for multiple-domain sentiment analysis. Previous approaches tackle this problem with transfer learning techniques, but they cannot handle multiple source domains and multiple target domains. This paper proposes a novel Bayesian probabilistic model to handle multiple source and multiple target domains. In this model, each word is associated with three factors: Domain label, domain dependence/independence and word polarity. We derive an efficient algorithm using Gibbs sampling for inferring the parameters of the model, from both labeled and unlabeled texts. Using real data, we demonstrate the effectiveness of our model in a document polarity classification task compared with a method not considering the differences between domains. Moreover our method can also tell whether each word's polarity is domain-dependent or domain-independent. This feature allows us to construct a word polarity dictionary for each domain.

The growing number of statistical topic models led to the need to better evaluate their output. Traditional evaluation means estimate the model’s fitness to unseen data. It has recently been proven than the output of human judgment can greatly differ from these measures. Thus the need for methods that better emulate human judgment is stringent. In this paper we present a system that computes the usefulness of individual topics from a given model on the basis of information drawn from a given ontology, in this case WordNet. The notion of utility is regarded as the ability to attribute a concept to each topic and separate words related to the topic from the unrelated ones based on that concept. In multiple experiments we prove the correlation between the automatic evaluation method and the answers received from human evaluators, for various corpora and difficulty levels. By changing the evaluation focus from a statistical one to a conceptual one we were able to detect which topics are conceptually meaningful and rank them accordingly.

Topic models have a wide range of applications, including modeling of text documents, images, user preferences, product rankings, and many others. However, learning optimal models may be difficult, especially for large problems. The reason is that inference techniques such as Gibbs sampling often converge to suboptimal models due to the abundance of local minima in large datasets. In this paper, we propose a general method of improving the performance of topic models. The method, called 'grouping transform', works by introducing auxiliary variables which represent assignments of the original model tokens to groups. Using these auxiliary variables, it becomes possible to resample an entire group of tokens at a time. This allows the sampler to make larger state space moves. As a result, better models are learned and performance is improved. The proposed ideas are illustrated on several topic models and several text and image datasets. We show that the grouping transform significantly improves performance over standard models.

Trained on the imbalanced labeled data, most classification Various semi-supervised learning methods have algorithms tend to predict test samples as the majority class been proposed recently to solve the longstanding and may ignore the minority class. Although many methods, shortage problem of manually labeled data in sentiment such as re-sampling [Chawla et al., 2002], one-class classification classification. However, most existing studies [Juszczak and Duin, 2003], and cost-sensitive assume the balance between negative and positive learning [Zhou and Liu, 2006], have been proposed to solve samples in both the labeled and unlabeled data, this issue, it is still unclear as to which method is more which may not be true in reality. In this paper, we suitable to handle the imbalanced problem in sentiment investigate a more common case of semi-supervised classification and whether the method is extendable to learning for imbalanced sentiment classification.