Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

A Performance Survey Of Text-Based Sentiment Analysis Methods For Automating Usability Evaluations

Usability testing, or user experience (UX) testing, is increasingly recognized as an important part of the user interface design process. However, evaluating usability tests can be expensive in terms of time and resources and can lack consistency between human evaluators. This makes automation an appealing expansion or alternative to conventional usability techniques. Early usability automation focused on evaluating human behavior through quantitative metrics but the explosion of opinion mining and sentiment analysis applications in recent decades has led to exciting new possibilities for usability evaluation methods. This paper presents a survey of modern, open-source sentiment analyzers’ usefulness in extracting and correctly identifying moments of semantic significance in the context of recorded mock usability evaluations. Though our results did not find a text-based sentiment analyzer that could correctly parse moments as well as human evaluators, one analyzer was found to be able to parse positive moments found through audio-only cues as well as human evaluators. Further research into adjusting settings on current sentiment analyzers for usability evaluations and using multimodal tools instead of text-based analyzers could produce valuable tools for usability evaluations when used in conjunction with human evaluators

A Short Survey on Deep Learning for Multimodal Integration: Applications, Future Perspectives and Challenges

Deep learning has achieved state-of-the-art performances in several research applications nowadays: from computer vision to bioinformatics, from object detection to image generation. In the context of such newly developed deep-learning approaches, we can define the concept of multimodality. The objective of this research field is to implement methodologies which can use several modalities as input features to perform predictions. In this, there is a strong analogy with respect to what happens with human cognition, since we rely on several different senses to make decisions. In this article, we present a short survey on multimodal integration using deep-learning methods. In a first instance, we comprehensively review the concept of multimodality, describing it from a two-dimensional perspective. First, we provide, in fact, a taxonomical description of the multimodality concept. Secondly, we define the second multimodality dimension as the one describing the fusion approaches in multimodal deep learning. Eventually, we describe four applications of multimodal deep learning to the following fields of research: speech recognition, sentiment analysis, forensic applications and image processing