Yanghui Rao

Building emotional dictionary for sentiment analysis of online news

Sentiment analysis of online documents such as news articles, blogs and microblogs has received increasing attention in recent years. In this article, we propose an efficient algorithm and three pruning strategies to automatically build a word-level emotional dictionary for social emotion detection. In the dictionary, each word is associated with the distribution on a series of human emotions. In addition, a method based on topic modeling is proposed to construct a topic-level dictionary, where each topic is correlated with social emotions. Experiment on the real-world data sets has validated the effectiveness and reliability of the methods. Compared with other lexicons, the dictionary generated using our approach is language-independent, fine-grained, and volume-unlimited. The generated dictionary has a wide range of applications, including predicting the emotional distribution of news articles, identifying social emotions on certain entities and news events.

2014 Special Issue: Community-aware user profile enrichment in folksonomy

In the era of big data, collaborative tagging (a.k.a. folksonomy) systems have proliferated as a consequence of the growth of Web 2.0 communities. Constructing user profiles from folksonomy systems is useful for many applications such as personalized search and recommender systems. The identification of latent user communities is one way to better understand and meet user needs. The behavior of users is highly influenced by the behavior of their neighbors or community members, and this can be utilized in constructing user profiles. However, conventional user profiling techniques often encounter data sparsity problems as data from a single user is insufficient to build a powerful profile. Hence, in this paper we propose a method of enriching user profiles based on latent user communities in folksonomy data. Specifically, the proposed approach contains four sub-processes: (i) tag-based user profiles are extracted from a folksonomy tripartite graph; (ii) a multi-faceted folksonomy graph is constructed by integrating tag and image affinity subgraphs with the folksonomy tripartite graph; (iii) random walk distance is used to unify various relationships and measure user similarities; (iv) a novel prototype-based clustering method based on user similarities is used to identify user communities, which are further used to enrich the extracted user profiles. To evaluate the proposed method, we conducted experiments using a public dataset, the results of which show that our approach outperforms previous ones in user profile enrichment.

2014 Special Issue: Affective topic model for social emotion detection

The rapid development of social media services has been a great boon for the communication of emotions through blogs, microblogs/tweets, instant-messaging tools, news portals, and so forth. This paper is concerned with the detection of emotions evoked in a reader by social media. Compared to classical sentiment analysis conducted from the writers perspective, analysis from the readers perspective can be more meaningful when applied to social media. We propose an affective topic model with the intention to bridge the gap between social media materials and a readers emotions by introducing an intermediate layer. The proposed model can be used to classify the social emotions of unlabeled documents and to generate a social emotion lexicon. Extensive evaluations using real-world data validate the effectiveness of the proposed model for both these applications.

Social emotion classification of short text via topic-level maximum entropy model

With the rapid proliferation of Web 2.0, the identification of emotions embedded in user-contributed comments at the social web is both valuable and essential. By exploiting large volumes of sentimental text, we can extract user preferences to enhance sales, develop marketing strategies, and optimize supply chain for electronic commerce. Pieces of information in the social web are usually short, such as tweets, questions, instant messages, messages, and news headlines. Short text differs from normal text because of its sparse word co-occurrence patterns, which hampers efforts to apply social emotion classification models. Most existing methods focus on either exploiting the social emotions of individual words or the association of social emotions with latent topics learned from normal documents. In this paper, we propose a topic-level maximum entropy (TME) model for social emotion classification over short text. TME generates topic-level features by modeling latent topics, multiple emotion labels, and valence scored by numerous readers jointly. The overfitting problem in the maximum entropy principle is also alleviated by mapping the features to the concept space. An experiment on real-world short documents validates the effectiveness of TME on social emotion classification over sparse words.

Towards building a social emotion detection system for online news

Abstract Social emotion detection of online users has become an important task for mining public opinions. Social emotion detection aims at predicting the readers’ emotions evoked by news articles, tweets, etc. In this article, we focus on building a social emotion detection system for online news. The system is built based on the modules of document selection, Part-of-speech (POS) tagging, and social emotion lexicon generation. Empirical studies are extensively conducted on a large scale real-world collection of news articles. Experiments show that the document selection algorithm has a positive effect on the social emotion detection. The system performs better with the words and POS combination compared to a feature set consisting only of words. POS is also useful to detect emotion ambiguity of words and the context dependence of their sentiment orientations. Furthermore, the proposed method of generating the lexicon outperforms the baselines in terms of social emotion prediction.

ZhihuRank: A Topic-Sensitive Expert Finding Algorithm in Community Question Answering Websites

Expert finding is important to the development of community question answering websites and e-learning. In this study, we propose a topic-sensitive probabilistic model to estimate the user authority ranking for each question, which is based on the link analysis technique and topical similarities between users and questions. Most of the existing approaches focus on the user relationship only. Compared to the existing approaches, our method is more effective because we consider the link structure and the topical similarity simultaneously. We use the real-world data set from Zhihu (a famous CQA website in China) to conduct experiments. Experimental results show that our algorithm outperforms other algorithms in the user authority ranking.

Multi-label maximum entropy model for social emotion classification over short text

Social media provides an opportunity for many individuals to express their emotions online. Automatically classifying user emotions can help us understand the preferences of the general public, which has a number of useful applications, including sentiment retrieval and opinion summarization. Short text is prevalent on the Web, especially in tweets, questions, and news headlines. Most of the existing social emotion classification models focus on the detection of user emotions conveyed by long documents. In this paper, we introduce a multi-label maximum entropy (MME) model for user emotion classification over short text. MME generates rich features by modeling multiple emotion labels and valence scored by numerous users jointly. To improve the robustness of the method on varied-scale corpora, we further develop a co-training algorithm for MME and use the L-BFGS algorithm for the generalized MME model. Experiments on real-world short text collections validate the effectiveness of these methods on social emotion classification over sparse features. We also demonstrate the application of generated lexicons in identifying entities and behaviors that convey different social emotions.

Term Weighting Schemes for Emerging Event Detection

As an event-based task, Emerging Event Detection (EED) faces the problems of multiple events on the same subject and the evolution of events. Current term weighting schemes for EED exploiting Named Entity, temporal information and Topic Modeling all have their limited utility. In this paper, a new term weighting scheme, which models the sparse aspect, global weight and local weight of each story, is proposed. Then, an unsupervised algorithm based on the new scheme is applied to EED. We evaluate our approach on two datasets from TDT5, and compare it with TFIDF and existing two schemes exploiting Topic Modeling. Experiments on Retrospective and On-line EED show that our scheme yields better results.

Weighted multi-label classification model for sentiment analysis of online news

With the extensive growth of social media services, many users express their feelings and opinions through news articles, blogs and tweets/microblogs. To discover the connections between emotions evoked in a user by varied-scale documents effectively, the paper is concerned with the problem of sentiment analysis over online news. Different from previous models which treat training documents uniformly, a weighted multi-label classification model (WMCM) is proposed by introducing the concept of “emotional concentration” to estimate the weight of training documents, in addition to tackle the issue of noisy samples for each emotion. The topic assignment is also used to distinguish different emotional senses of the same word at the semantic level. Experimental evaluations using short news headlines and long documents validate the effectiveness of the proposed WMCM for sentiment prediction.

Discover learning path for group users : a profile-based approach

Abstract With the explosion of knowledge and information in the big data era, learning new things efficiently is of crucial significance. Despite recent development of e-learning techniques which have broken the temporal and spatial barriers for learners, it is still very difficult to meet the requirement of efficient learning, as the key issues involve not only searching for learning resources but also identification of learning paths. People from diverse backgrounds, in most cases, also need to work as a group to acquire new knowledge or skills and complete certain tasks. As these tasks are normally assigned with time constraints, employment of e-learning systems may be the optimal approach. In this research, we study the issue of identifying a suitable learning path for a group of learners rather than a single learner in an e-learning environment. Particularly, a profile-based framework for the discovery of group learning paths is proposed by taking various learning-related factors into consideration. We also conduct experiments on real learners to validate the effectiveness of the proposed approach.