human-computer interaction

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Use of human-computer interaction, where the device can detect and respond appropriately to the emotions and other stimuli of its user, has changed the computing experience. The concept of virtual reality and human emotion is relatively new, and, therefore, previous studies do not examine it. Recent studies align themselves to supporting the claim that affective computing improves the human experience. More specifically, studies have shown that training systems which understand the human experience improve learning the process by recognizing that emotional status of the user influences the learning experience. For this reason, this literature review is carried out under the epidemiological philosophy that affective computing is effective in improving the human experience. The literature analyzes studies carried out on the subject by critically examining arguments presented before concluding by giving the summary of the dominant points from the literature discussed.

Evaluation of Studies on Affective Computing

Reading the emotions of a computer user has been taken to a higher level with the advancement in technology.  The National Research Council of Canada investigators carried out an analysis of the NRC- sentiment analysis system, to found out its functioning in detecting sentiment of word or phrase and sentiment of a short informal textual message. The investigation was more deceptive, where they aimed at making a full explanation of the systems mode of functioning, and knowledge (National Research Council of Canada 46). The evaluation of this system revealed that it can derive exciting features from sentiment lexicons of high novel coverage, and it creates a different sentiment lexicon for negated words, making it possible to generate a dictionary from tweets without the intervention of human (47). The interesting facts revelation by the National Research Council of Canada, presents a good description of the core aspects of the affective computing, but it does show direct link between the human experience and enhanced ability to read affective cues that are present in complex patterns that include facial expression, posture, gestures tone of voice, and autonomic nervous system measures. Thus, the study leaves a knowledge gap concerning the human experience derived from the advanced affective computing, and this study will seek to fill this gap.

Albeit significant attention given to words polarity, resulting in the creation of large polarity lexicons, the emotion analysis research relies on limited emotion lexicons. Mohammad and Turney tried to minimize this research limitation by elaborating the way in which integration of wisdom and strength of the crowds (through crowdsourcing) can be tapped into to come up with a very high-quality, word-polarity and word-emotion lexicon in a cheap and quick way (29). However, despite finding cloud sourcing as a useful tool for getting high-quality word-emotion, the study does not identify the satisfaction level that individuals can get from using computing systems with high level of emotion lexical. Like, National Research Council of Canada and Mohammad and Turney delved much into examining the structure, model and framework of crowd computing and affection computing and does little to look at the overall experience that can be derived from this, which is the objective. Our study will try to integrate between the engineering component of the affective computing and the human experience.

There are many topics trying to address the issue of affective computing and most specifically that address themselves into the human- and machine-oriented research. Scherer, Tanja, and Etienne developed a compilation of a diverse range of topics touching on the affective computing (1). The compilation, (made up of nineteen chapter) focusses on topics at the frontier of computer science and cognitive and presents a fascinating insights into the ways in which one can design a computer system which can recognize and communicate emotional states. Even, though the compilation is more descriptive that it is experimental, it brings together input of specialists from neuroscience, psychology, AI, and philosophy, thereby, representing an attempt to ground the affe……………

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