Open AccessSentiment Analysis For Customer service
Author(s) -
E. Kodhai,
B. nivetha,
K. sriakila,
G. suvalakshmi
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.d7287.049420
Subject(s) - computer science , automatic summarization , customer intelligence , service (business) , sentiment analysis , categorization , information retrieval , customer service , natural language processing , voice of the customer , short message service , customer relationship management , artificial intelligence , customer retention , service quality , database , telecommunications , economy , economics
NLP can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. By utilizing Natural Language Processing the customer experience will improve . E-mail is still the most commonly used digital customer service channel 54% of customers have used E-mail customer service channel. The proposal of this paper is to develop an algorithm where customer E-mails are scanned, analyze the sentiment from the body of the message and automate customer e-mail categorization and prioritization for the banking sector. The main goals are to collect bank query related E-mail data, ranging from general information, escalations and request, Develop a machine learning algorithm that can perform text mining and sentimental analysis, Provide priority based categorized information for management to prioritize and improve customer service.