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VIStopic:A visual analytics system for making sense of large document collections using hierarchical topic modeling

Visual Informatics 2017年第1期1卷 40-47页

作者： Yi Yang Quanming Yao Huamin Qu Hong Kong University of Science and Technology Hong Kong Lenovo Group Limited Hong Kong

Effective analysis of large text collections remains a challenging problem given the growing volume of available text ***,text mining techniques have been rapidly developed for automatically extracting key information from massive text *** modeling,as one of the novel techniques that extracts a thematic structure from documents,is widely used to generate text summarization and foster an overall understanding of the corpus *** powerful,this technique may not be directly applicable for general analytics scenarios since the topics and topic-document relationship are often presented probabilistically in ***,information that plays an important role in knowledge discovery,for example,times and authors,is hardly reflected in topic modeling for comprehensive *** this paper,we address this issue by presenting a visual analytics system,VIStopic,to help users make sense of large document collections based on topic *** first extracts a set of hierarchical topics using a novel hierarchical latent tree model(HLTM)(Liu et al.,2014).In specific,a topic view accounting for the model features is designed for overall understanding and interactive exploration of the topic *** leverage multi-perspective information for visual analytics,VIStopic further provides an evolution view to reveal the trend of topics and a document view to show details of topical *** case studies based on the dataset of IEEE VIS conference demonstrate the effectiveness of our system in gaining insights from large document collections.

关键词： topic-modeling Text visualization Visual analytics

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A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

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Computers, Materials & Continua 2023年第10期77卷 115-137页

作者： Bishrul Haq Sher Muhammad Daudpota Ali Shariq Imran Zenun Kastrati Waheed Noor Department of Computer Science Sukkur IBA UniversitySukkur65200Pakistan Department of Computer Science Norwegian University of Science and Technology(NTNU)Gjøvik2815Norway Department of Informatics Linnaeus UniversityVäxjö35195Sweden Department of Computer Science&IT University of BalochistanQuetta87300Pakistan

The Internet has become one of the significant sources for sharing information and expressing users’opinions about products and their interests with the associated *** is essential to learn about product reviews;however,to react to such reviews,extracting aspects of the entity to which these reviews belong is equally ***-based Sentiment Analysis(ABSA)refers to aspects extracted from an opinionated *** literature proposes different approaches for ABSA;however,most research is focused on supervised approaches,which require labeled datasets with manual sentiment polarity labeling and aspect *** study proposes a semisupervised approach with minimal human supervision to extract aspect terms by detecting the aspect ***,the study deals with two main sub-tasks in ABSA,named Aspect Category Detection(ACD)and Aspect Term Extraction(ATE).In the first sub-task,aspects categories are extracted using topic modeling and filtered by an oracle further,and it is fed to zero-shot learning as the prompts and the augmented *** predicted categories are the input to find similar phrases curated with extracting meaningful phrases(e.g.,Nouns,Proper Nouns,NER(Named Entity Recognition)entities)to detect the aspect *** study sets a baseline accuracy for two main sub-tasks in ABSA on the Multi-Aspect Multi-Sentiment(MAMS)dataset along with SemEval-2014 Task 4 subtask 1 to show that the proposed approach helps detect aspect terms via aspect categories.

关键词： Natural language processing sentiment analysis aspect-based sentiment analysis topic-modeling POS tagging zero-shot learning

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