Machine learning model for semantic search .

Application need to be hosted as a web service and can have its own database to operate upon. Application will expose the API to be called. There are three aspects - When we look for duplicate issues - that happens within the sprint When we look for repetitive issues that happens within the team but the search will in all past retrospectives that has happened for the team. Is the suggestion - which is based on similar issues within different teams across the organizations Data - The data is collected through Retrospective that happens after every sprint. Every team member will join the meeting and enter one or more “What went well” and “things to improve”. Once the team has entered the data team reviews each item. For every things to improve team come upon a remediation which may or may not have an action item. The application is divided into three phases: Sentiment analysis: Algorithm should be able to predict the sentiment associated with the text. It should give an output predicting the sentiment score.