ReUS: A Real-Time Unsupervised System For Monitoring Opinion Streams

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.
solutions in order to make a system ecient for
real-time tasks and at the same time
exible in order to
adopt it in any domain without requiring the training
of sentiment models. Finally, the deployment of such
system into real-world scenarios needs the development
of usable solutions for accessing and analyzing data.
Methods 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.
Results 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 dataset used in likewise editions
of SemEval. Second, a user group has been invited
to judge the usability of the platform.
Conclusion The developed platform demonstrated to be
suitable for being used into real-world scenarios requiring
(i) the capability of processing real-time opinionbased
documents streams and (ii) the availability of
usable facilities for analyzing and visualizing collected
data. Examples of possible analysis and visualizations
includes the presentation of lists ranking aspects by
their importance of by their polarity values computed
within the whole data repository. This kind of analysis
enables, for instance, the discovery of product issues.