Jun Yang

The topic model has been widely applied to various research domains such as information retrieval, data mining, and so on. It can discover topics of texts in an unsupervised way. In the early years, most researches mainly focused on long texts. With the emergence of the Internet, the number of short texts is growing rapidly. Most existing schemes to solve the sparsity problems of short texts, are mainly based on data aggregation or model improvements. Among them, the Biterm Topic Model is one of the most representative models. It proposed a new way to model topics based on document-level word pairs and has shown creativity and effectiveness. However, this strategy ignores those semantically similar and rarely co-occurrent word pairs. What’s more, most researches ignore the multi-sense phenomenon in natural languages. In this paper, we utilize multi-sense word vectors to extract similar word pairs from the whole corpus by considering multiple senses. Based on this idea, we introduce a novel short-text topic model, which disambiguates multiple senses of words and generates more reasonable global biterms. Experimental results on two open-source English datasets have shown superiority to state-of-the-art topic models.

Physical distancing between individuals is key to preventing the spread of a disease such as COVID-19. On the one hand, having access to information about physical interactions is critical for decision makers; on the other, this information is sensitive and can be used to track individuals. In this work, we design Poirot, a system to collect aggregate statistics about physical interactions in a privacy-preserving manner. We show a preliminary evaluation of our system that demonstrates the scalability of our approach even while maintaining strong privacy guarantees.

Fine-grained sentiment analysis is a useful tool for producers to understand consumers’ needs as well as complaints about products and related aspects from online platforms. In this article, we define a novel task named “Multi-Entity Aspect-Based Sentiment Analysis (ME-ABSA)”. It investigates the sentiment towards entities and their related aspects. It makes the well-studied aspect-based sentiment analysis a special case of this type, where the number of entities is limited to one. We contribute a new dataset for this task, with multi-entity Chinese posts in it. We propose to model context, entity, and aspect memory to address the task and incorporate dependency information for further improvement. Experiments show that our methods perform significantly better than baseline methods on datasets for both ME-ABSA task and ABSA task. The in-depth analysis further validates the effectiveness of our methods and shows that our methods are capable of generalizing to new (entity, aspect) combinations with little loss of accuracy. This observation indicates that data annotation in real applications can be largely simplified.

Web attacks are increasing and the scale of malicious URL continues to expand with the rapid development of the Internet, so that the network security situation is increasingly grim. In this case, this paper studies the URL multi-classification problem, which is a continuation of the reference [1] and follows the data sets and most of feature selection methods in it. Firstly, different types of URL structure is analyzed and features which have obvious directivity to the attack type are increased based on the original features. Secondly, an improved semi-supervised algorithm is proposed to train the URL multi-classification model. Finally, the effect of the multi-classification model is verified by using the data set with different labeling rate, which shows the improved algorithm has good classification performance compared to the previous algorithm.

The traditional data publishing methods will remove the sensitive attributes and generate the abundant records to achieve the goal of privacy protection. In the big data environment, they cannot satisfy some data mining tasks with privacy considerations. This paper provides a new data publishing framework. It can preserve the data integrity, i.e., the original data structure is preserved, and it doesn't require deleting any attribute and adding k-times data to achieve anonymity.

The booming development of Online Social Networks (OSNs) provides a novel way of expressing emotions. Research on emotion analysis for tweets in OSNs is becoming increasingly popular in recent years. Traditional emotion analysis only classifies one tweet into a single emotion category. However, in reality, a tweet may belong to several different emotion categories. In the paper, our goal is to predict all emotion labels of each tweet. We use graphic emoticons, punctuation expressions together with a tiny but accurate lexicon to label data and provide a Multi-label Emotion Classification algorithm (MEC) for tweets in Weibo (so called Chinese Twitter). Our method has superior performance to the state-of-the-art method under both single-label and multi-label evaluation measures. We also carried out a case study on Weibo dataset of Malaysia Missing Flight. We came to several meaningful conclusions such as "The outbreak of Anger has a delay after breaking point of Sadness".