Sentiment-Topic Modelling in Text Mining

Peer reviewed

A Curated Corpus for Sentiment-Topic Analysis

Postprint

Sentiment-Topic Modelling in Text Mining

Peer reviewed

LDA-TM : A two-step approach to twitter topic data clustering

The Twitter System is the biggest social network in the world, and everyday millions of tweets are posted and talked about, expressing various views and opinions. A large variety of research activities have been conducted to study how the opinions can be clustered and analyzed, so that some tendencies can be uncovered. Due to the inherent weaknesses of the tweets - very short texts and very informal styles of writing - it is rather hard to make an investigation of tweet data analysis giving results with good performance and accuracy. In this paper, we intend to attack the problem from another aspect - using a two-layer structure to analyze the twitter data: LDA with topic map modelling. The experimental results demonstrate that this approach shows a progress in twitter data analysis. However, more experiments with this method are expected in order to ensure that the accurate analytic results can be maintained.

Exploring social representations of adapting to climate change using topic modeling and Bayesian networks

When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on in order to act. Social representations theory suggests how individuals and society make sense of the unfamiliar and hence how the resultant social representations (SRs) cognitively, emotionally, and actively orient people and enable communication. SRs are social constructions that emerge through individual and collective engagement with media and with everyday conversations among people. Recent developments in text analysis techniques, and in particular topic modeling, provide a potentially powerful analytical method to examine the structure and content of SRs using large samples of narrative or text. In this paper I describe the methods and results of applying topic modeling to 660 micronarratives collected from Australian academics / researchers, government employees, and members of the public in 2010-2011. The narrative fragments focused on adaptation to climate change (CC) and hence provide an example of Australian society making sense of an emerging and conflict ridden phenomena. The results of the topic modeling reflect elements of SRs of adaptation to CC that are consistent with findings in the literature as well as being reasonably robust predictors of classes of action in response to CC. Bayesian Network (BN) modeling was used to identify relationships among the topics (SR elements) and in particular to identify relationships among topics, sentiment, and action. Finally the resulting model and topic modeling results are used to highlight differences in the salience of SR elements among social groups. The approach of linking topic modeling and BN modeling offers a new and encouraging approach to analysis for ongoing research on SRs.

Recognition of Activities of Daily Living from Topic Model

Research in ubiquitous and pervasive technologies have made it possible to recognise activities of daily living through non-intrusive sensors. The data captured from these sensors are required to be classified using various machine learning or knowledge driven techniques to infer and recognise activities. The process of discovering the activities and activity-object patterns from the sensors tagged to objects as they are used is critical to recognising the activities. In this paper, we propose a topic model process of discovering activities and activity-object patterns from the interactions of low level state-change sensors. We also develop a recognition and segmentation algorithm to recognise activities and recognise activity boundaries. Experimental results we present validates our framework and shows it is comparable to existing approaches.

Relations among Topic Knowledge, Individual Interest, and Relational Reasoning, and Critical Thinking in Maternity Nursing

Critical thinking in learners is a goal of educators and professional organizations in nursing as well as other professions. However, few studies in nursing have examined the role of the important individual difference factors topic knowledge, individual interest, and general relational reasoning strategies in predicting critical thinking. In addition, most previous studies have used domain-general, standardized measures, with inconsistent results. Moreover, few studies have investigated critical thinking across multiple levels of experience. The major purpose of this study was to examine the degree to which topic knowledge, individual interest, and relational reasoning predict critical thinking in maternity nurses. For this study, 182 maternity nurses were recruited from national nursing listservs explicitly chosen to capture multiple levels of experience from prelicensure to very experienced nurses. The three independent measures included a domain-specific Topic Knowledge Assessment (TKA), consisting of 24 short-answer questions, a Professed and Engaged Interest Measure (PEIM), with 20 questions indicating level of interest and engagement in maternity nursing topics and activities, and the Test of Relational Reasoning (TORR), a graphical selected response measure with 32 items organized in scales corresponding to four forms of relational reasoning: analogy, anomaly, antithesis, and antinomy. The dependent measure was the Critical Thinking Task in Maternity Nursing (CT2MN), composed of a clinical case study providing cues with follow-up questions relating to nursing care. These questions align with the cognitive processes identified in a commonly-used definition of critical thinking in nursing. Reliable coding schemes for the measures were developed for this study. Key findings included a significant correlation between topic knowledge and individual interest. Further, the three individual difference factors explained a significant proportion of the variance in critical thinking with a large effect size. While topic knowledge was the strongest predictor of critical thinking performance, individual interest had a moderate significant effect, and relational reasoning had a small but significant effect. The findings suggest that these individual difference factors should be included in future studies of critical thinking in nursing. Implications for nursing education, research, and practice are discussed.

A Fuzzy Decision Strategy for Topic Identification and Dynamic Selection of Language Models

International audience

The representation of engineering education as a social media topic on Twitter

Online social media systems have created new ways for individuals to communicate, share information and interact with a wide audience. For organisations, social media provide new avenues for communication and collaboration with their stakeholders. The potential value of social media tools to assist in the successful communication and marketing inside and outside of engineering organisations has been identified. In the context of engineering education, the potential of social media to open new modes of communication, interaction and experimentation between students and teachers has also been identified, and a limited number of examples can be found documented in the literature. One of the most widely-used social media tools is the ‘microblogging’ service Twitter. This research presents an analysis of nearly 19,000 tweets relating to ‘engineering education’ collected over a period of almost a year. Social network analysis is used to visualise the Twitter data. The Twitter social media communication is examined to identify who is active on this topic, who is influential, and what is the structure of the online conversations relating to engineering education. This work provides insights regarding how engineering education is currently represented in social media internationally, and offers a methodology to those interested in related future research.

Topic extraction from microblog posts using conversation structures

Conventional topic models are ineffective for topic extraction from microblog messages since the lack of structure and context among the posts renders poor message-level word co-occurrence patterns. In this work, we organize microblog posts as conversation trees based on reposting and replying relations, which enrich context information to alleviate data sparseness. Our model generates words according to topic dependencies derived from the conversation structures. In specific, we differentiate messages as leader messages, which initiate key aspects of previously focused topics or shift the focus to different topics, and follower messages that do not introduce any new information but simply echo topics from the messages that they repost or reply. Our model captures the different extents that leader and follower messages may contain the key topical words, thus further enhances the quality of the induced topics. The results of thorough experiments demonstrate the effectiveness of our proposed model.

Property cash flow studies : focusing on model consistency and data accuracy

The high degree of variability and inconsistency in cash flow study usage by property professionals demands improvement in knowledge and processes. Until recently limited research was being undertaken on the use of cash flow studies in property valuations but the growing acceptance of this approach for major investment valuations has resulted in renewed interest in this topic. Studies on valuation variations identify data accuracy, model consistency and bias as major concerns. In cash flow studies there are practical problems with the input data and the consistency of the models. This study will refer to the recent literature and identify the major factors in model inconsistency and data selection. A detailed case study will be used to examine the effects of changes in structure and inputs. The key variable inputs will be identified and proposals developed to improve the selection process for these key variables. The variables will be selected with the aid of sensitivity studies and alternative ways of quantifying the key variables explained. The paper recommends, with reservations, the use of probability profiles of the variables and the incorporation of this data in simulation exercises. The use of Monte Carlo simulation is demonstrated and the factors influencing the structure of the probability distributions of the key variables are outline. This study relates to ongoing research into functional performance of commercial property within an Australian Cooperative Research Centre.

Relationship between indoor/outdoor concentrations of particles : a critical review

The relationship between indoor and outdoor concentration levels of particles in the absence and in the presence of indoor sources has been attracting an increasing level of attention. Understanding of the relationship and the mechanisms driving it, as well as the ability to quantify it, are of importance for assessment of source contribution, assessment of human exposure and for control and management of particles. It became particularly important to address this topic when evidence came from epidemiological studies on the close association between outdoor concentration levels of particles and health effects, yet with many studies showing that indoor concentrations could be significantly higher than those outdoors. This paper presents a summary of an extensive literature review on this topic conducted with an aim to identify general trends in relation to the I/O relationship emerging from studies conducted worldwide. The review considered separately a larger body of papers published on PM10, PM2.5, as well as the smaller database on particle number and number or volume size distribution. A specific focus of this paper is on naturally ventilated houses. The conclusion from the review is that despite the multiplicity of factors that play role in affecting the relationship, there are clear trends emerging in relation to the I/O relationship for particle mass concentration, enabling more general predictions to be made about the relationship. However, more research is still needed on particle number concentration and size distribution.

Is philanthropy relevant? a study of professional advisers in Australia

CPNS’ new Working Paper examines the latest findings from a national survey of professional advisers on the topic of philanthropy assistance to high-net-worth individuals. Findings confirm a growing willingness within the Australian advisory sector to provide philanthropy advice to clients with means, although the rate of change has slowed since 2005. Advisers are now more likely to see benefits in providing this type of counselling, as well as benefits for their clients in engaging in philanthropy.

Working at the interface : the descriptive relevance of Grunig and Hunt’s theories to public relations practices in south east Queensland schools

This article examines the relevance of James Grunig and Todd Hunt’s (1984) theories to public relations practitioners’ roles in south east Queensland schools. It focuses in particular on the two-way symmetric model in this context. The geographical boundaries of the research mean that this article is intended primarily as an exploratory, descriptive analysis of a specific area rather than an exhaustive treatise on the general topic of public relations in Australian schools. However, it is hoped that it will prove useful in identifying bases for further study and discussion.