Emotion Expression Extraction Method for Chinese Microblog Sentences

With the rapid spread of Chinese microblog, a large number of microblog topics are being generated in real-time. More and more users pay attention to emotion expressions of these opinionated sentences in different topics. It is challenging to label the emotion expressions of opinionated sentences manually. For this endeavor, an emotion expression extraction method is proposed to process millions of user-generated opinionated sentences automatically in this paper. Specifically, the proposed method mainly contains two tasks: emotion classification and opinion target extraction. We first use a lexicon-based emotion classification method to compute different emotion values in emotion label vectors of opinionated sentences. Then emotion label vectors of opinionated sentences are revised by an unsupervised emotion label propagation algorithm. After extracting candidate opinion targets of opinionated sentences, the opinion target extraction task is performed on a random walk-based ranking algorithm, which considers the connection between candidate opinion targets and the textual similarity between opinionated sentences, ranks candidate opinion targets of opinionated sentences. Experimental results demonstrate the effectiveness of algorithms in the proposed method

What changed your mind : the roles of dynamic topics and discourse in argumentation process

In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the in- creasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful -- topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations

A Hotspot Discovery Method Based on Improved FIHC Clustering Algorithm

It was difficult to find the microblog hotspot because the characteristics of microblog were short, rapid, change and so on. A microblog hotspot detection method based on MFIHC and TOPSIS was proposed in order to solve the problem. Firstly, the calculation of HowNet similarity was used in the score function of FIHC, the semantic links between frequent words were considered, and the initial clusters based on frequent words were produced more accurately. Then the initial cluster of the text repletion of mircoblog was reduced, and the idea of Single-Pass clustering was used to the reduced topic cluster in order to get the Hotspot. At last, an improved TOPSIS model was used to sort the hot topics in order to get the rank of the hot topics. Compared with the other text clustering algorithms and hotspot detection methods, the method has good effect, and can be a more comprehensive response to the current hot topics

Identification of Online Users' Social Status via Mining User-Generated Data

With the burst of available online user-generated data, identifying online users’ social status via mining user-generated data can play a significant role in many commercial applications, research and policy-making in many domains. Social status refers to the position of a person in relation to others within a society, which is an abstract concept. The actual definition of social status is specific in terms of specific measure indicator. For example, opinion leadership measures individual social status in terms of influence and expertise in an online society, while socioeconomic status characterizes personal real-life social status based on social and economic factors. Compared with traditional survey method which is time-consuming, expensive and sometimes difficult, some efforts have been made to identify specific social status of users based on specific user-generated data using classic machine learning methods. However, in fact, regarding specific social status identification based on specific user-generated data, the specific case has several specific challenges. However, classic machine learning methods in existing works fail to address these challenges, which lead to low identification accuracy. Given the importance of improving identification accuracy, this thesis studies three specific cases on identification of online and offline social status. For each work, this thesis proposes novel effective identification method to address the specific challenges for improving accuracy. The first work aims at identifying users’ online social status in terms of topic-sensitive influence and knowledge authority in social community question answering sites, namely identifying topical opinion leaders who are both influential and expert. Social community question answering (SCQA) site, an innovative community question answering platform, not only offers traditional question answering (QA) services but also integrates an online social network where users can follow each other. Identifying topical opinion leaders in SCQA has become an important research area due to the significant role of topical opinion leaders. However, most previous related work either focus on using knowledge expertise to find experts for improving the quality of answers, or aim at measuring user influence to identify influential ones. In order to identify the true topical opinion leaders, we propose a topical opinion leader identification framework called QALeaderRank which takes account of both topic-sensitive influence and topical knowledge expertise. In the proposed framework, to measure the topic-sensitive influence of each user, we design a novel influence measure algorithm that exploits both the social and QA features of SCQA, taking into account social network structure, topical similarity and knowledge authority. In addition, we propose three topic-relevant metrics to infer the topical expertise of each user. The extensive experiments along with an online user study show that the proposed QALeaderRank achieves significant improvement compared with the state-of-the-art methods. Furthermore, we analyze the topic interest change behaviors of users over time and examine the predictability of user topic interest through experiments. The second work focuses on predicting individual socioeconomic status from mobile phone data. Socioeconomic Status (SES) is an important social and economic aspect widely concerned. Assessing individual SES can assist related organizations in making a variety of policy decisions. Traditional approach suffers from the extremely high cost in collecting large-scale SES-related survey data. With the ubiquity of smart phones, mobile phone data has become a novel data source for predicting individual SES with low cost. However, the task of predicting individual SES on mobile phone data also proposes some new challenges, including sparse individual records, scarce explicit relationships and limited labeled samples, unconcerned in prior work restricted to regional or household-oriented SES prediction. To address these issues, we propose a semi-supervised Hypergraph based Factor Graph Model (HyperFGM) for individual SES prediction. HyperFGM is able to efficiently capture the associations between SES and individual mobile phone records to handle the individual record sparsity. For the scarce explicit relationships, HyperFGM models implicit high-order relationships among users on the hypergraph structure. Besides, HyperFGM explores the limited labeled data and unlabeled data in a semi-supervised way. Experimental results show that HyperFGM greatly outperforms the baseline methods on individual SES prediction with using a set of anonymized real mobile phone data. The third work is to predict social media users’ socioeconomic status based on their social media content, which is useful for related organizations and companies in a range of applications, such as economic and social policy-making. Previous work leverage manually defined textual features and platform-based user level attributes from social media content and feed them into a machine learning based classifier for SES prediction. However, they ignore some important information of social media content, containing the order and the hierarchical structure of social media text as well as the relationships among user level attributes. To this end, we propose a novel coupled social media content representation model for individual SES prediction, which not only utilizes a hierarchical neural network to incorporate the order and the hierarchical structure of social media text but also employs a coupled attribute representation method to take into account intra-coupled and inter-coupled interaction relationships among user level attributes. The experimental results show that the proposed model significantly outperforms other stat-of-the-art models on a real dataset, which validate the efficiency and robustness of the proposed model