Topic Detection Model Research Articles

基于情感时间序列的微博热点主题检测

Twitter plays a significant role in information diffusion, and it has evolved to become an important information resource as well as news feed. There is a widespread interest in what is happening on Twitter, and the instantaneous news information that is passed on. However, with the large amount of data, it is impossible to manually determine what topic is trending, which makes real-time topic detection attractive and significant. Furthermore, Twitter provides a platform for the sharing of opinions and providing feedback for events, news, and products, etc. Because users tend to express their real thoughts on Twitter, it is recognized as a valuable source of opinions. Nevertheless, most works about trending topic detection fail to consider sentiments. In this work, we develop a non-parametric supervised real-time-trending topic-detection model with a sentimental feature. By performing experiments, we show that our model successfully detects trending sentimental topic in a short time. After applying a combination of multiple features, e.g., tweet volume and user volume, the proposed model demonstrates impressive effectiveness with an 82.3% recall rate, surpassing all of the competitors.

An Improved Topic Detection Method for Chinese Microblog Based On Incremental Clustering

A topic detection model based on hierarchical clustering for Chinese microblog is proposed in this paper. In order to minimize the impact of noise, we optimize the feature selection and weight computation method and use a new scoring method to filter out those topic-unrelated tweets. We also give an improved topic detection algorithm which uses a new vector distance calculation method and center vector updating method. It is shown by the experiment that this method can filter out majority of the topic-unrelated tweets and identify microblog topics accurately and efficiently. The study of microblog topic detection method can help users and service providers find out microblog hot topics dynamically.

Keep It Simple with Time: A Reexamination of Probabilistic Topic Detection Models

Topic detection (TD) is a fundamental research issue in the Topic Detection and Tracking (TDT) community with practical implications; TD helps analysts to separate the wheat from the chaff among the thousands of incoming news streams. In this paper, we propose a simple and effective topic detection model called the temporal Discriminative Probabilistic Model (DPM), which is shown to be theoretically equivalent to the classic vector space model with feature selection and temporally discriminative weights. We compare DPM to its various probabilistic cousins, ranging from mixture models like von-Mises Fisher (vMF) to mixed membership models like Latent Dirichlet Allocation (LDA). Benchmark results on the TDT3 data set show that sophisticated models, such as vMF and LDA, do not necessarily lead to better results; in the case of LDA, notably worst performance was obtained under variational inference, which is likely due to the significantly large number of LDA model parameters involved for document-level topic detection. On the contrary, using a relatively simple time-aware probabilistic model such as DPM suffices for both offline and online topic detection tasks, making DPM a theoretically elegant and effective model for practical topic detection.

Variable space hidden Markov model for topic detection and analysis

Discovering topics from large amount of documents has become an important task recently. Most of the topic models treat document as a word sequence, whether in discrete character or term frequency form. However, the number of words in a document is greatly different from that in other documents. This will lead to several problems for current topic models in dealing with topics analysis. On the other hand, it is difficult to perform topic transition analysis based on current topic models. In an attempt to overcome these deficiencies, a variable space hidden Markov model (VSHMM) is proposed to represent the topics, and several operations based on space computation are presented. A hierarchical clustering algorithm with dynamically changing of the component number in topic model is proposed to demonstrate the effectiveness of the VSHMM. Method of document partition based on topic transition is also present. Experiments on a real-world dataset show that the VSHMM can improve the accuracy while decreasing the algorithm’s time complexity greatly compared with the algorithm based on current mixture model.

Dependency structure language model for topic detection and tracking

In this paper, we propose a new language model, namely, a dependency structure language model, for topic detection and tracking (TDT) to compensate for weakness of unigram and bigram language models. The dependency structure language model is based on the Chow expansion theory and the dependency parse tree generated by a linguistic parser. So, long-distance dependencies can be naturally captured by the dependency structure language model. We carried out extensive experiments to verify the proposed model on topic tracking and link detection in TDT. In both cases, the dependency structure language models perform better than strong baseline approaches.