BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation

Existing topic modeling and text segmentation methodologies generally require large datasets for training, limiting their capabilities when only small collections of text are available. In this work, we reexamine the inter-related problems of “topic identification” and “text segmentation” for sparse document learning, when there is a single new text of interest. In developing a methodology to handle single documents, we face two major challenges. First is sparse information : with access to only one document, we cannot train traditional topic models or deep learning algorithms. Second is significant noise : a considerable portion of words in any single document will produce only noise and not help discern topics or segments. To tackle these issues, we design an unsupervised, computationally efficient methodology called Biclustering Approach to Topic modeling and Segmentation (BATS). BATS leverages three key ideas to simultaneously identify topics and segment text: (i) a new mechanism that uses word order information to reduce sample complexity, (ii) a statistically sound graph-based biclustering technique that identifies latent structures of words and sentences, and (iii) a collection of effective heuristics that remove noise words and award important words to further improve performance. Experiments on six datasets show that our approach outperforms several state-of-the-art baselines when considering topic coherence, topic diversity, segmentation, and runtime comparison metrics.

Integrating Big Data Into Evaluation: R Code for Topic Identification and Modeling

Despite the rising popularity of big data, there is speculation that evaluators have been slow adopters of these new statistical approaches. Several possible reasons have been offered for why this is the case: ethical concerns, institutional capacity, and evaluator capacity and values. In this method note, we address one of these barriers and aim to build evaluator capacity to integrate big data analytics into their studies. We focus our efforts on a specific topic modeling technique referred to as latent Dirichlet allocation (LDA) because of the ubiquitousness of qualitative textual data in evaluation. Given current equity debates, both within evaluation and the communities in which we practice, we analyze 1,796 tweets that use the hashtag #equity with the R packages topicmodels and ldatuning to illustrate the use of LDA. Furthermore, a freely available workbook for implementing LDA topic modeling is provided as Supplemental Material Online.

Evaluasi Topik Tersembunyi Berdasarkan Aspect Extraction menggunakan Pengembangan Latent Dirichlet Allocation

Recently, Sentiment Analysis is used for expression detection of products or services. Sentiment Analysis is one category type with a level of aspect focused on extracting product aspects. One of the common methods used for aspect extraction is Latent Dirichlet Allocation (LDA) using random topic identification, but this method has not been able to find an acceptable topic with some aspects having been found. Undeterminable topics are referred to as the hidden topics. This study purpose is to evaluate and compare the suitability of identifying hidden topics between human and computer evaluation. The study is also focused on aspect extraction using a variety of LDA innovations. The data used in this study used case studies on e-Commerce. Data were processed using feature selection and grouped using LDA development. Then the data results are processed using Latent Topic Identification based on subjective and objective evaluations. The identification of hidden topic results was evaluated using several semantic and lexicon tests. The evaluation results indicate the comparison of two hidden topic identification assessment values is quite relevant with the average difference in value reaching 6%. As a result, computer calculations assist humans in determining topics if each topic has a low coherence value.

Evaluating Cultural Impact in Discursive Space through Digital Footprints

The research presented in this paper describes an evaluation of the impact of spatial interventions in public spaces, measured by social media data. This contribution aims at observing the way a spatial intervention in an urban location can affect what people talk about on social media. The test site for our research is Domplatz in the center of Hamburg, Germany. In recent years, several actions have taken place there, intending to attract social activity and spotlight the square as a landmark of cultural discourse in the city of Hamburg. To evaluate the impact of this strategy, textual data from the social networks Twitter and Instagram (i.e., tweets and image captions) are collected and analyzed using Natural Language Processing intelligence. These analyses identify and track the cultural topic or “people talking about culture” in the city of Hamburg. We observe the evolution of the cultural topic, and its potential correspondence in levels of activity, with certain intervention actions carried out in Domplatz. Two analytic methods of topic clustering and tracking are tested. The results show a successful topic identification and tracking with both methods, the second one being more accurate. This means that it is possible to isolate and observe the evolution of the city’s cultural discourse using NLP. However, it is shown that the effects of spatial interventions in our small test square have a limited local scale, rather than a city-wide relevance.

Supporting youth 12–24 during the COVID-19 pandemic: how Foundry is mobilizing to provide information, resources and hope across the province of British Columbia

Foundry is a province-wide network of integrated health and social service centres for young people aged 12–24 in British Columbia (BC), Canada. Online resources and virtual care broaden Foundry’s reach. Its online platform – foundrybc.ca – offers information and resources on topics such as mental health, sexual wellness, life skills, and other content suggested by youth and young adults. The COVID-19 pandemic has presented significant and unique challenges to the youth and their families/caregivers served by Foundry. Disruptions to school, access to essential healthcare services such as counselling, familial financial security and related consequences has left young people with heightened anxiety. The Foundry team mobilized to respond to these extenuating circumstances and support BC youth and their families/caregivers during this hard time through three goals: (1) to amplify (and translate for young people and their families/caregivers) key messages released by government to support public health responses to the COVID-19 pandemic; (2) to develop content that supports the needs of young people and their families/caregivers that existed before COVID-19 and are likely to be exacerbated as a result of this pandemic; and (3) to develop and host opportunities through social media and website articles to engage young people and their families/caregivers by creating a sense of community and promoting togetherness and social connection during the COVID-19 pandemic. Each goal and plan integrated the leadership, feedback and needs of youth and their families through engagement with Foundry’s provincial youth and family advisory committees. Our study evaluated Foundry’s media response to the COVID-19 pandemic by recording/measuring (1) the website/social content created, including emerged thematic topic areas; (2) the process of topic identification through engagement with youth and young adults; (3) the social and website analytics of the created content; and (4) the constant, critical team-reflection of our response to the pandemic. Following measurement and reflection, our team offers recommendations to health promotion organizations for future preparedness.

Teaching Research Methodology: Student-centered Approach Computing Education Undergraduate Course

Teaching research methodology is one of the core components of various degree programs. Scholarship around teaching research methodology is beginning to grow with most work concentrated in Social Work, Health, Social Science, and limited work in the area computing education. This paper presents a reflection on adopting a student centred approach towards teaching research methodology course to three different groups of fourth level university students. In this paper the strategies for facilitating deep learning in teaching research methods and research methods in the Information Systems domain will be discussed. In addition, reflections on the use of a student centred approach, student experiences and strategies used. The experiences and strategies relate to facilitating deep learning. The experiences are focused on topic identification, conducting, and writing up the literature reviews, developing an understanding of the research methodology inclusive of data analysis and presenting the research report. Doi: 10.28991/esj-2021-01255 Full Text: PDF