ReUS: a Real-time Unsupervised System For Monitoring Opinion Streams

被引:0
作者
Mauro Dragoni
Marco Federici
Andi Rexha
机构
[1] Fondazione Bruno Kessler,
[2] University of Amsterdam,undefined
[3] Know-Center,undefined
来源
Cognitive Computation | 2019年 / 11卷
关键词
Sentiment analysis; Opinion mining; Unsupervised aspect extraction; Real-time monitoring;
D O I
暂无
中图分类号
学科分类号
摘要
An actual challenge within the sentiment analysis research area is the extraction of polarity values associated with specific aspects (or opinion targets) contained in user-generated content. This task, called aspect-based sentiment analysis, brings new challenges like the disambiguation of words’ role within a text and the inference of correct polarity values based on the domain in which a text occurs. The former requires strategies able to understand how each word is used in a specific context in order to annotate it as aspect or not. The latter need to be addressed with unsupervised solutions in order to make a system efficient for real-time tasks and at the same time flexible in order to adopt it in any domain without requiring the training of sentiment models. Finally, the deployment of such a system into real-world scenarios needs the development of usable solutions for accessing and analyzing data. This paper presents the ReUS platform: a system integrating an unsupervised approach, based on open information extraction strategies, for performing real-time aspect-based sentiment analysis together with facilities supporting decision-makers in the analysis and visualization of collected data. The ReUS platform has been validated from a quantitative and qualitative perspectives. First, the aspect extraction and polarity inference capabilities have been evaluated on three datasets used in likewise editions of SemEval. Second, a user group has been invited to judge the usability of the platform. The developed platform demonstrated to be suitable for being used into real-world scenarios requiring (i) the capability of processing real-time opinion-based documents streams and (ii) the availability of usable facilities for analyzing and visualizing collected data. Examples of possible analysis and visualizations include the presentation of lists ranking aspects by the importance of their polarity values computed within the whole data repository. This kind of analysis enables, for instance, the discovery of product issues.
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页码:469 / 488
页数:19
相关论文
共 104 条
[1]  
Poria S(2017)A review of affective computing from unimodal analysis to multimodal fusion Information Fusion 37 98-125
[2]  
Cambria E(2018)Distinguishing between facts and opinions for sentiment analysis: survey and challenges Information Fusion 44 65-77
[3]  
Bajpai R(2017)Multilingual sentiment analysis: from formal to informal and scarce resource languages Artif Intell Rev 48 499-527
[4]  
Hussain A(2018)Learning multi-grained aspect target sequence for chinese sentiment analysis Knowl-Based Syst 148 167-176
[5]  
Chaturvedi I(2017)Lexicon generation for emotion analysis of text IEEE Intell Syst 32 102-108
[6]  
Cambria E(2018)OntoSenticNet: a commonsense ontology for sentiment analysis IEEE Intell Syst 33 77-85
[7]  
Welsch R(2016)Statistical learning theory and ELM for big social data analysis IEEE Comput Intell Mag 11 45-55
[8]  
Herrera F(2018)Semi-supervised learning for big social data analysis Neurocomputing 275 1662-1673
[9]  
Lo SL(2017)Learning word representations for sentiment analysis Cogn Comput 9 843-851
[10]  
Cambria E(2018)Recent trends in deep learning based natural language processing IEEE Comput Intell Mag 13 55-75