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Recently, rapid growth of social networks and online news resources from Internet have made text stream clustering become an insufficient application in multiple domains (e.g.: text retrieval diversification, social event detection, text summarization, etc.) Different from traditional static text clustering approach, text stream clustering task has specific key challenges related to the rapid change of topics/clusters and high-velocity of coming streaming document batches. Recent well-known model-based text stream clustering models, such as: DTM, DCT, MStream, etc. are considered as word-independent evaluation approach which means largely ignoring the relations between words while sampling clusters/topics. It definitely leads to the decrease of overall model accuracy performance, especially for short-length text documents such as comments, microblogs, etc. in social networks. To tackle these existing problems, in this paper we propose a novel approach of graph-of-words (GOWs) based text stream clustering, called GOW-Stream. The application of common GOWs which are generated from each document batch while sampling clusters/topics can support to overcome the word-independent evaluation challenge. Our proposed GOW-Stream is promising to significantly achieve better text stream clustering performance than recent state-of-the-art baselines. Extensive experiments on multiple benchmark real-world datasets demonstrate the effectiveness of our proposed model in both accuracy and time-consuming performances. [ABSTRACT FROM AUTHOR]

Storyline generation aims to produce a concise summary of related events unfolding over time from a collection of news articles. It can be cast into an evolutionary clustering problem by separating news articles into different epochs. Existing unsupervised approaches to storyline generation are typically based on probabilistic graphical models. They assume that the storyline distribution at the current epoch depends on the weighted combination of storyline distributions in the latest previous M epochs. The evolutionary parameters of such long-term dependency are typically set by a fixed exponential decay function to capture the intuition that events in more recent epochs have stronger influence to the storyline generation in the current epoch. However, we argue that the amount of relevant historical contextual information should vary for different storylines. Therefore, in this paper, we propose a new Dynamic Dependency Storyline Extraction Model (D 2 SEM) in which the dependencies among events in different epochs but belonging to the same storyline are dynamically updated to track the time-varying distributions of storylines over time. The proposed model has been evaluated on three news corpora and the experimental results show that it outperforms the state-of-the-art approaches and is able to capture the dependency on historical contextual information dynamically. [ABSTRACT FROM AUTHOR]

In multi-label classification settings, one of the most common problems is the massive label output space. To alleviate this, some methods opt to exploit label correlations to reduce the output space during prediction. However, these methods sacrifice efficiency or ignore global label correlations. In addition, label imbalances are another problem that is prevalent in multi-label classification. Current methods of correcting for imbalance oftentimes use single-label methods, which fail to consider label correlations. In this paper, we introduce general frameworks that incorporate topic modeling to seamlessly address both problems. We show that these frameworks can allow even the most naïve methods, such as Binary Relevance, to perform similarly to state-of-the-art methods. Furthermore, we show that our frameworks can also adapt state-of-the-art methods to perform better than the methods by themselves. [ABSTRACT FROM AUTHOR]

The volume of short text data increases rapidly these years. Data examples include tweets and online Q&A pairs. It is essential to organize and summarize these data automatically. Topic model is one of the effective approaches, whose application domains include text mining, personalized recommendation and so on. Conventional models like pLSA and LDA are designed for long text data. However, these models may suffer from the sparsity problem brought by lacking words in short text scenarios. Recent studies such as BTM show that using word co-occurrent pairs is effective to relieve the sparsity problem. However, both BTM and extended models ignore the quantifiable relationship between words. From our perspectives, two more related words should occur in the same topic. Based on this idea, we introduce a model named RIBS, which makes use of RNN to learn relationship. By using the learned relationship, we introduce a model named RIBS-Bigrams, which can display topics with bigrams. Through experiments on two open-source and real-world datasets, RIBS achieves better coherence in topic discovery, and RIBS-Bigrams achieves better readability in topic display. In the document characterization task, the document representation of RIBS can lead better purity and entropy in clustering, higher accuracy in classification. [ABSTRACT FROM AUTHOR]

The volume of short text data increases rapidly these years. Data examples include tweets and online Q&A pairs. It is essential to organize and summarize these data automatically. Topic model is one of the effective approaches, whose application domains include text mining, personalized recommendation and so on. Conventional models like pLSA and LDA are designed for long text data. However, these models may suffer from the sparsity problem brought by lacking words in short text scenarios. Recent studies such as BTM show that using word co-occurrent pairs is effective to relieve the sparsity problem. However, both BTM and extended models ignore the quantifiable relationship between words. From our perspectives, two more related words should occur in the same topic. Based on this idea, we introduce a model named RIBS, which makes use of RNN to learn relationship. By using the learned relationship, we introduce a model named RIBS-Bigrams, which can display topics with bigrams. Through experiments on two open-source and real-world datasets, RIBS achieves better coherence in topic discovery, and RIBS-Bigrams achieves better readability in topic display. In the document characterization task, the document representation of RIBS can lead better purity and entropy in clustering, higher accuracy in classification. [ABSTRACT FROM AUTHOR]

As an efficient sampling algorithm for latent dirichlet allocation SparseLDA uses cache strategy to improve the time and space efficiency of its standard gibbs sampling algorithm (StdGibbs) by recycling previous computation. However, SparseLDA cannot further improve the time-efficiency of StdGibbs, since the amount of recycled computation is limited. This is because the word types of two adjacent tokens are usually different and the previous computation cannot be further recycled easily. To solve this problem, in this paper we propose a new algorithm named Efficient SparseLDA (ESparseLDA) based on SparseLDA. The main idea of ESparseLDA is to first rearrange the tokens within one text according to the word types so that the tokens of the same word type are aggregated together and then recycle more computation while making no approximation and ensuring the exactness. In this paper, we make detailed theoretical explanations and comparative experimental analyses on the correctness, exactness and time-efficiency of ESparseLDA. In detail, the statistical significance tests on perplexities strictly show that ESparseLDA is correct and exact. In addition, the running time results show that the time-efficiency of ESparseLDA is the higher than SparseLDA in varying degrees from 5.06% to 31.85% on the different datasets used in experiments. [ABSTRACT FROM AUTHOR]

One of the prevalent studies on Topic Detection and Tracking (TDT) is topic evolution. With the emergence of Internet data, there is a clear need for intuitive and adaptive methods to analyze a series of evolutions. Most approaches completely depend on topic models and only focus on whether topics are changed while ignoring the degree of changes, resulting in poor quality topics and insensitivity of changes. In this paper, we propose a framework of topic evolution based on semantic connections which not only indicates the content similarity between documents but also shows the time decay for an adaptive number of topics and rapid responses to the changes of contents. Additionally, semantic connection features can be used to visualize topic evolution, which makes the analyses much easier. For empirical studies, three data sets in real applications are chosen to prove the effectiveness of our method, and the results show that our method has a better performance in reducing redundant topics, avoiding topic suppression, and discerning the vanishment of old topics and the appearance of new topics. [ABSTRACT FROM AUTHOR]

Nowadays in China, Sina Weibo has become the most popular microblog platform and researches about it are proposed increasingly. In this paper, the problem of emotion classification of Weibo's posts is addressed in a hierarchical way using a constrained topic model and Support Vector Regression (SVR). Based on this topic model which is variation of Latent Dirichlet Allocation (LDA), an implicit emotion detection algorithm is proposed to identify the underlying emotions. Meanwhile, the constraints are generated based on prior knowledge extraction approaches to compact LDA in order to generate domain-specified topics. Furthermore, a hierarchical emotion structure is employed to classify emotions more precisely into 19 classes. This hierarchy can meet different research granularities. The whole architecture is proposed aimed at alleviating the pain of misclassification caused by feature imbalance and decreasing the labor cost. The experiment results validate that our model outperforms traditional methods with precision, recall and F-scores. [ABSTRACT FROM AUTHOR]

The large amount of text on the Internet cause people hard to understand the meaning in a short limit time. Topic models (e.g. LDA and PLSA) have then been proposed to summarize the long text into several topic terms. In the recent years, the short text media such as Twitter is very popular. However, directly applying the transitional topic model on the short text corpus usually obtains non-coherent topics. It's because that there is no enough words to discover the word co-occurrence patterns in a short document. In this paper, we solve the problem of lack of the local word co-occurrence in LDA. Thus, we proposed an improvement of word co-occurrence method to enhance the topic models. We generate new virtual documents by re-organizing the words in documents and use it to enhance the traditional LDA. The experimental results show that our re-organized LDA (RO-LDA) method gets better results in the noisy Tweet dataset and the regular news dataset. Moreover, in our proposed augmented model, we do not need any external data. Our proposed methods are only based on the original topic model, thus our methods can easily apply to other existing LDA based models. [ABSTRACT FROM AUTHOR]