3 research outputs found
Customer sentiment analysis from audio data using deep learning
No full textCall Centers or Support Centers in different companies aggregate huge amount of audio data everyday. From all the conversations, few conversations demonstrate the disappointment of
clients towards services, products or delivery. Finding the sentiment of the customer helps in determining whether the customer was satisfied with the service, product or not. However, manually analyzing the huge amount of audio data is time consuming, laborious, and burdensome. The aim of this research is to develop the deep learning based model which would enable to automatically evaluate the sentiment of a customer throughout a conversation with a call center agent.
In this work, we developed Long Term Short Memory (LSTM) based deep learning models for customer audio call data analysis. The proposed pipeline consists of two sequential steps: a) Audio transcription: speech recognition of the conversations and document them in text; and b) Sentiment Analysis: conduct the sentiment analysis on the text data using Natural Language Processing (NLP). We compute spectrogram features from audio data and then fine-tune the LSTM based Deep Speech Model using customer call data. Deep Speech model can successfully transcribe the conversations between client and call center agent in a text form. Then we compute the 1-gram feature from text data which find the occurrence of the words responsible to identify the customer sentiments. We fed this feature into a LSTM based deep architecture which would enable to detect customer sentiments from text data. Recent advances in natural language understanding and generation facilitates to detect customer sentiments successfully as accurate as human experts.
Both speech transcription and sentiment analysis part of the proposed tool are very generic in nature which could utilize for other audio data transcription and text sentiment analysis purposes. Tools were developed in python which can be easily transported and adapted in other programming environments
Deep learning based models for software effort estimation using story points in agile environments
No full textIn the era of agile software development methodologies, traditional planning and software effort estimation methods are replaced to meet customer’s satisfaction in agile environments. However, software effort estimation remains a challenge. Although teams have achieved better accuracy in estimating story points effort required to implement user stories or issues, these estimations mostly rely on subjective assessments, leading to inaccuracy and impacting software project delivery. Some researchers are pointing good results by the adoption of deep learning to address this issue. Given the foregoing, this study proposes deep learning-based models for story points estimation in agile projects. Different algorithms are proposed and trained over a large dataset for story points estimation made by 16 open-source projects. In addition, we take advantage of natural language processing techniques to excavate better features from the software requirements written as user stories
Proceedings of Intelligent Computing and Technologies Conference
Get PDFThis proceeding contains articles on the various research ideas of the academic community and practitioners presented at the Intelligent Computing and Technologies Conference (ICTCon2021). ICTCon2021 was jointly organized by Assam Science and Technology University (ASTU), and Central Institute of Technology Kokrajhar (CITK) on March 15th–16th, 2021.
Conference Title: Intelligent Computing and Technologies ConferenceConference Acronym: ICTCon2021Conference Date: 15–16 March 2021Conference Location: Online (Virtual Mode)Conference Organizers: Assam Science and Technology University (ASTU) and Central Institute of Technology Kokrajhar (CITK)
