Emoticons speak louder than letters!
Problem being addressed
Many industries are curious to know about the voice of customers about their products and hence mine customer's reviews. They collect the reviews through social media, customer service conversations, NPS and surveys. They analyse the sentiments of the customer's reviews to know how good their product is, and/or how bad their product is. Bad reviews are also helpful which helps to know which feature of product the customer is not satisfied about. This will eventually help to improve their business. Now customers are using more emoticons not only just text. Emoticons express more than the words itself. For example consider these sentences: 1. I love this product 😃 2. I love this product 😏 When analysing only text's sentiments, both looks positive about the product. However, when we include emoticons for the analysis, the first sentence (1.) says positively about the product whereas the second sentence (2.) says the customer is not happy with the product. It is essential to eliminate the discrepancy in the analysis by including emoticons along with text.
The solution is simple. While analysing text sentiment, it is important to include sentiment as well. Following is the list of letter to represent different emoticons. Emoticon set: Happiness :-D, =D, xD, (ˆ ˆ) Sadness :-(, =( Crying :’(, =’(, (; ;) Boredom - -, -.-, (> <) Love <3, (L) Embarrassment :-$, =$, >///< These inclusion helps in identifying even sarcastic reviews and can improve the over all sentiment accuracy.
Advantages of this solution
Performance is illustrated in the paper mentioned. The analysis with emoticons has achieved 93.94% accuracy whereas analysis without emoticons has achieved only 22.02% accuracy.
Possible New Application of the Work
This can be applied in advertising, where companies particularly value the voice of customers to improve the business.
This can be applied in finance, where companies particularly value the voice of customers to improve the business.
This can be applied in ecommerce, where companies particularly value the voice of customers to improve the business.
Travel and Tourism Industry
This can be applied in travel, where companies particularly value the voice of customers to improve the business.
Source DOI: #############
Source URL: #############