Sentiment Analysis For Customer service
E.kodhai1, B.nivetha2, K.sriakila3, G.suvalakshmi4
1E.kodhai*, computer science and engineering, sri manakula vinayagar engineering college, pudhucherry, india.
2B.nivetha, computer science and engineering, sri manakula vinayagar engineering college, pudhucherry, india.
3K.sriakila, computer science and engineering, sri manakula vinayagar engineering college, pudhucherry, india.
4G.suvalakshmi, computer science and engineering, sri manakula vinayagar engineering college, pudhucherry, india.
Manuscript received on March 05, 2020. | Revised Manuscript received on March 16, 2020. | Manuscript published on April 30, 2020. | PP: 585-589 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7287049420/2020©BEIESP | DOI: 10.35940/ijeat.7287.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: E-mail categorization, priority, sentiment analysis.