A Review on Overcoming the Challenges of Sentiment Analysis- Tone and Framing

Abstract

As we all know that how meteoric the social network is growing across the world. By taking this as an advantage many product-based companies have made their products supremacy. The underlaying paradigm of this growth is about the applications of natural language processing. As there were uncountable number of applications in NLP, today in this we will chew over about sentiment analysis- “precisely it’s an emotion detector”. We all know that the individuals express their feelings by emotions and it’s a complex task to detect their emotion. In those situations, using this sentiment analysis is beneficial to find the emotion behind their expression or even behind their sentences. Not only from the emotion, but also extracting subjective information from the sentences you speak. So, what else is needed for the companies to invest after knowing about their customers emotion. Once the sentiment analysis model is defined using various machine learning algorithms, but the model later faces so many challenges which will affect the final prediction. These are the few challenges A we should more concern about- tone related issues, appropriate polarity, exact domain analysis-how to work on different domains, sarcasm behind your text and emojis- these days people more expressing their emotion using emotions than words so, the model might get confused by seeing this emojis. By these you might get know that the challenges are also the most important which we should care about. This paper will apprise you about how to overcome completely from these challenges. Mainly focuses on the tone related issues that sentiment analysis model is facing. Keywords: social network, natural language processing, emotion, sentiment analysis, opinion, sentiment, extracting, subjective information, prediction, tone, polarity, domain, sarcasm, apprise, analysis, automatic model and challenges.