Abstract: Sentimental analysis and emotion detection have been an ever-growing field in academic literature in recent years [1,2,3]. There are many methods and techniques to distinguish positive and negative tokens as well as classification of emotions respectively. However, the use of tone indicators has been relatively underexplored within the field.
Tone indicators are a relatively recent trend in social media. Users denote a positive or negative connotation as well as an emotion in a sentence at the moment of conception with syntax such as “/s,” “/pos,” and “/neg.” These annotations often are context-free, or do not depend on previously declared information, and allow for a possible method of study for sentence-level analysis.
This method may help achieve significantly higher certainty and a way to analyze emotions users on social media could be sharing. This paper will argue the benefits of the possible exploration of utilizing tone indicators in sentiment analysis and emotion detection with advantages, accessibility, and possible applications.
Index Terms: Tone Indicators, Machine Learning, Sentimental analysis, Emotion Recognition