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Articles 1 - 12 of 12
Full-Text Articles in Databases and Information Systems
Variational Graph Author Topic Modeling, Ce Zhang, Hady Wirawan Lauw
Variational Graph Author Topic Modeling, Ce Zhang, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
While Variational Graph Auto-Encoder (VGAE) has presented promising ability to learn representations for documents, most existing VGAE methods do not model a latent topic structure and therefore lack semantic interpretability. Exploring hidden topics within documents and discovering key words associated with each topic allow us to develop a semantic interpretation of the corpus. Moreover, documents are usually associated with authors. For example, news reports have journalists specializing in writing certain type of events, academic papers have authors with expertise in certain research topics, etc. Modeling authorship information could benefit topic modeling, since documents by the same authors tend to reveal …
Do Sequels Outperform Or Disappoint? Insights From An Analysis Of Amazon Echo Consumer Reviews, Kyong Jin Shim, Siaw Ling Lo, Su Yee Liew
Do Sequels Outperform Or Disappoint? Insights From An Analysis Of Amazon Echo Consumer Reviews, Kyong Jin Shim, Siaw Ling Lo, Su Yee Liew
Research Collection School Of Computing and Information Systems
Rapid technological advances in recent years drastically transformed our world. Amidst modern technological inventions such as smart phones, smart watches and smart home devices, consumers of electronic digital devices experience greatly improved automation, productivity, and efficiency in conducting routine daily tasks, information searching, shopping as well as finding entertainment. In the last few years, the global smart speaker market has undergone significant growth. As technology continues to advance and smart speakers are equipped with innovative features, the adoption of smart speakers will increase and so will consumer expectations. This research paper presents an aspect-specific sentiment analysis of consumer reviews of …
Mining Informal & Short Student Self-Reflections For Detecting Challenging Topics: A Learning Outcomes Insight Dashboard, De Lin Ong, Gottipati Swapna, Siaw Ling Lo, Venky Shankararaman
Mining Informal & Short Student Self-Reflections For Detecting Challenging Topics: A Learning Outcomes Insight Dashboard, De Lin Ong, Gottipati Swapna, Siaw Ling Lo, Venky Shankararaman
Research Collection School Of Computing and Information Systems
Having students write short self-reflections at the end of each weekly session enables them to reflect on what they have learnt in the session and topics they find challenging. Analysing these self-reflections provides instructors with insights on how to address the missing conceptions and misconceptions of the students and appropriately plan and deliver the next session. Currently, manual methods adopted to analyse these student reflections are time consuming and tedious. This paper proposes a solution model that uses content mining and NLP techniques to automate the analysis of short self-reflections. We evaluate the solution model by studying its implementation in …
Discovery Of Mental Wellness Via Social Analytics For Liveability In An Urban City, Kar Way Tan
Discovery Of Mental Wellness Via Social Analytics For Liveability In An Urban City, Kar Way Tan
Research Collection School Of Computing and Information Systems
Smart cities, are often perceived as urban areas that use technologies to manage resources, improve economy and enhance community livelihood. In this paper, we share an approach which uses multiple sources of data for evidence-based analysis of the public's views, concerns and sentiments on the topic related to mental wellness. We hope to bring forth a better understanding of the existing concerns of the citizens and available social support. Our study leverages on social sensing via text mining and social network analysis to listen to the voices of the citizens through revealed content from web data sources, such as social …
Exploring The Impact Of Covid-19 On Aviation Industry: A Text Mining Approach, Gottipati Swapna, Kyong Jin Shim, Weiling Angeline Jiang, Sheng Wei Andre Justin Lee
Exploring The Impact Of Covid-19 On Aviation Industry: A Text Mining Approach, Gottipati Swapna, Kyong Jin Shim, Weiling Angeline Jiang, Sheng Wei Andre Justin Lee
Research Collection School Of Computing and Information Systems
Our study presents a comprehensive analysis of news articles from FlightGlobal website during the first half of 2020. Our analyses reveal useful insights on themes and trends concerning the aviation industry during the COVID-19 period. We applied text mining and NLP techniques to analyse the articles for extracting the aviation themes and article sentiments (positive and negative). Our results show that there is a variation in the sentiment trends for themes aligned with the real-world developments of the pandemic. The article sentiment analysis can offer industry players a quick sense of the nature of developments in the industry. Our article …
Topicsummary: A Tool For Analyzing Class Discussion Forums Using Topic Based Summarizations, Swapna Gottipati, Venky Shankararaman, Renjini Ramesh
Topicsummary: A Tool For Analyzing Class Discussion Forums Using Topic Based Summarizations, Swapna Gottipati, Venky Shankararaman, Renjini Ramesh
Research Collection School Of Computing and Information Systems
This Innovative Practice full paper, describes the application of text mining techniques for extracting insights from a course based online discussion forum through generation of topic based summaries. Discussions, either in classroom or online provide opportunity for collaborative learning through exchange of ideas that leads to enhanced learning through active participation. Online discussions offer a number of benefits namely providing additional time to reflect and synthesize information before writing, providing a natural platform for students to voice their ideas without any one student dominating the conversation, and providing a record of the student’s thoughts. An online discussion forum provides a …
Assessing The Language Of Chat For Teamwork Dialogue, Antonette Shibani, Elizabeth Koh, Vivian Lai, Kyong Jin Shim
Assessing The Language Of Chat For Teamwork Dialogue, Antonette Shibani, Elizabeth Koh, Vivian Lai, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
In technology enhanced language learning, many pedagogical activities involve students in online discussion such as synchronous chat, in order to help them practice their language skills. Besides developing the language competency of students, it is also crucial to nurture their teamwork competencies for today's global and complex environment. Language communication is an important glue of teamwork. In order to assess the language of chat for teamwork dimensions, several text mining methods are pos sible. However, difficulties arise such as pre-processing being a black box and classification approaches and algorithms being dependent on the context. To address these issues, the study …
Aspect-Based Helpfulness Prediction For Online Product Reviews, Yinfei Yang, Cen Chen, Forrest Sheng Bao
Aspect-Based Helpfulness Prediction For Online Product Reviews, Yinfei Yang, Cen Chen, Forrest Sheng Bao
Research Collection School Of Computing and Information Systems
Product reviews greatly influence purchase decisions in online shopping. A common burden of online shopping is that consumers have to search for the right answers through massive reviews, especially on popular products. Hence, estimating and predicting the helpfulness of reviews become important tasks to directly improve shopping experience. In this paper, we propose a new approach to helpfulness prediction by leveraging aspect analysis of reviews. Our hypothesis is that a helpful review will cover many aspects of a product at different emphasis levels. The first step to tackle this problem is to extract proper aspects. Because related products share common …
Author Topic Model-Based Collaborative Filtering For Personalized Poi Recommendations, Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, Tao Mei
Author Topic Model-Based Collaborative Filtering For Personalized Poi Recommendations, Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, Tao Mei
Research Collection School Of Computing and Information Systems
From social media has emerged continuous needs for automatic travel recommendations. Collaborative filtering (CF) is the most well-known approach. However, existing approaches generally suffer from various weaknesses. For example, sparsity can significantly degrade the performance of traditional CF. If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information for effective inference. Moreover, existing recommendation approaches often ignore rich user information like textual descriptions of photos which can reflect users' travel preferences. The topic model (TM) method is an effective way to solve the "sparsity problem," but is still far …
Mining Generalized Associations Of Semantic Relations From Textual Web Content, Tao Jiang, Ah-Hwee Tan, We Wang
Mining Generalized Associations Of Semantic Relations From Textual Web Content, Tao Jiang, Ah-Hwee Tan, We Wang
Research Collection School Of Computing and Information Systems
Traditional text mining techniques transform free text into flat bags of words representation, which does not preserve sufficient semantics for the purpose of knowledge discovery. In this paper, we present a two-step procedure to mine generalized associations of semantic relations conveyed by the textual content of Web documents. First, RDF (resource description framework) metadata representing semantic relations are extracted from raw text using a myriad of natural language processing techniques. The relation extraction process also creates a term taxonomy in the form of a sense hierarchy inferred from WordNet. Then, a novel generalized association pattern mining algorithm (GP-Close) is applied …
Blocking Reduction Strategies In Hierarchical Text Classification, Ee Peng Lim, Aixin Sun, Wee-Keong Ng, Jaideep Srivastava
Blocking Reduction Strategies In Hierarchical Text Classification, Ee Peng Lim, Aixin Sun, Wee-Keong Ng, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
One common approach in hierarchical text classification involves associating classifiers with nodes in the category tree and classifying text documents in a top-down manner. Classification methods using this top-down approach can scale well and cope with changes to the category trees. However, all these methods suffer from blocking which refers to documents wrongly rejected by the classifiers at higher-levels and cannot be passed to the classifiers at lower-levels. We propose a classifier-centric performance measure known as blocking factor to determine the extent of the blocking. Three methods are proposed to address the blocking problem, namely, threshold reduction, restricted voting, and …
Knowledge Discovery From Texts: A Concept Frame Graph Approach, Kanagasabai Rajaraman, Ah-Hwee Tan
Knowledge Discovery From Texts: A Concept Frame Graph Approach, Kanagasabai Rajaraman, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
We address the text content mining problem through a concept based framework by constructing a conceptual knowledge base and discovering knowledge therefrom. Defining a novel representation called the Concept Frame Graph (CFG), we propose a learning algorithm for constructing a CFG knowledge base from text documents. An interactive concept map visualization technique is presented for user-guided knowledge discovery from the knowledge base. Through experimental studies on real life documents, we observe that the proposed approach is promising for mining deeper knowledge.