Mukund Srinath (left on photo) and Pranav Venkit (right). In their paper The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis, Pranav Venkit and Mukund Srinath, and co-authors Sanjana Gautam, Saranya Venkatraman, Vipul Gupta, Rebecca J. Passonneau and Shomir Wilson, present a review of the sociotechnical aspects of sentiment analysis. In this interview, Pranav and Mukund tell us more about sentiment analysis, how they went about surveying the literature, and recommendations for researchers in the field. Sentiment analysis, often referred to as opinion mining, is a branch of natural language processing (NLP) that focuses on determining and extracting the emotional tone or sentiment expressed in text data, such as reviews, social media posts, or any written content. This is the cumulative brief definition that is most commonly used in NLP.

The importance and pervasiveness of emotions in our lives makes affective computing a tremendously important and vibrant line of work. Systems for automatic emotion recognition (AER) and sentiment analysis can be facilitators of enormous progress (e.g., in improving public health and commerce) but also enablers of great harm (e.g., for suppressing dissidents and manipulating voters). Thus, it is imperative that the affective computing community actively engage with the ethical ramifications of their creations. In this paper, I have synthesized and organized information from AI Ethics and Emotion Recognition literature to present fifty ethical considerations relevant to AER. Notably, the sheet fleshes out assumptions hidden in how AER is commonly framed, and in the choices often made regarding the data, method, and evaluation. Special attention is paid to the implications of AER on privacy and social groups. The objective of the sheet is to facilitate and encourage more thoughtfulness on why to automate, how to automate, and how to judge success well before the building of AER systems. Additionally, the sheet acts as a useful introductory document on emotion recognition (complementing survey articles).

Several high-profile events, such as the use of biased recidivism systems and mass testing of emotion recognition systems on vulnerable sub-populations, have highlighted how technology will often lead to more adverse outcomes for those that are already marginalized. In this paper, I will make a case for thinking about ethical considerations not just at the level of individual models and datasets, but also at the level of AI tasks. I will present a new form of such an effort, Ethics Sheets for AI Tasks, dedicated to fleshing out the assumptions and ethical considerations hidden in how a task is commonly framed and in the choices we make regarding the data, method, and evaluation. Finally, I will provide an example ethics sheet for automatic emotion recognition. Together with Data Sheets for datasets and Model Cards for AI systems, Ethics Sheets aid in the development and deployment of responsible AI systems.