Most existing Sentiment analysis approaches prioritise on identifying sentiment of movie reviews or similar type of texts (product reviews, twitter posts etc). The review data utilised in these investigations are written as single narratives with no interaction between the authors or speakers.
With the advance in technology and availability of large-scale user data from social networking services such as Whatsapp, Twitter and Wechat, conversational messaging has risen as a popular means of communication among people. As a result, a significant number of interactive texts have been created, each of which contains a wealth of subjective data.
Lot of organizations rely on phone calls and audio-visual customer interaction to provide, monitor, and evaluate their services, such as banks, insurance companies and education sectors. Using sentiment analysis on customer interaction call and help determine corelation of sentiment between specific agents or teams that generate high or low customer satisfaction, it can help agents to flag calls that require attention based on customer sentiment, it can also help understand the impact of the length of the call associated with satisfied and unsatisfied customers.
Using sentiment analysis in more sensitive places like healthcare or emergency service presents extra challenges, as the model’s prediction can contribute to a life-or-death situation when used to help look for signs of vulnerability or deception.