Sentiment analysis and real-time microblog search

This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment

A Net Loan Monitoring Platform for University Students Based on Visual Micro-blog

In order to prevent college students from tragic events, which mostly result from falling into illegal net loan, a net loan risk monitoring platform of college students was established based on the microblog visualization. Based on the calculation model of risk degree (CMRD) and the calculation model of relational closeness (CMRC), a visual system, a user relationship analysis system and an early warning system were constructed on the platform. Through the CMRD, the risk degree of micro-blog net loan contents can be worked out to decide whether warning behaviors are necessary. Through the CMRC, the closeness of net loan bloggers with users can be obtained a propagation map. The key nodes can be cut off by internet security officers in time, which can prevent further propagation of the contents of the microblog net loan. The platform can effectively alleviate the tense situation of college students and illegal online loans

Cyberemotions in the Era of New Media

The introduction of new media has accelerated changes in the generation and delivery of information. Cyberemotions, as a new type of public opinion, can be more contagious and propagate in more diverse ways than traditional public sentiments. The emotions of internet users have a rising influence on the progression of recent significant social events. In order to spark additional discussion on online sentiment modulation and online public opinion tracking, this article presented an overview of cyberemotions definition and key analytical methodologies in existing online sentiment research, as well as a synopsis of major components of cyberemotions

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

A Time-Aware Approach to Improving Ad-hoc Information Retrieval from Microblogs

There is an immense number of short-text documents produced as the result of microblogging. The content produced is growing as the number of microbloggers grows, and as active microbloggers continue to post millions of updates. The range of topics discussed is so vast, that microblogs provide an abundance of useful information. In this work, the problem of retrieving the most relevant information in microblogs is addressed. Interesting temporal patterns were found in the initial analysis of the study. Therefore the focus of the current work is to first exploit a temporal variable in order to see how effectively it can be used to predict the relevance of the tweets and, then, to include it in a retrieval weighting model along with other tweet-specific features. Generalized Linear Mixed-effect Models (GLMMs) are used to analyze the features and to propose two re-ranking models. These two models were developed through an exploratory process on a training set and then were evaluated on a test set