INTRODUCTION

1. P.U.R.E Research Symposium Computer Science Department Procid facilitates consensus building by supporting the participants in understanding the current content and status of the discussion, keeping track of the solution alternatives, evaluating the solution alternatives, maintaining a welcoming atmosphere, and inviting other members to join the discussion. INTRODUCTION OBJECTIVE MATERIALS AND METHODS I collected 10% of the total comments on the website as a sample and get the analyze result for every comment and put all of my data into the result file. Once I finished the analyses of the sample data and did some research about the result sample of the data, I can clearly find the distribution a new comment locates in among the whole dataset. By using the SDK provided by Alchemapi.com, It will return a XML containing the score of one comment. Positive score means positive comment, negative score means negative comment. CONCLUSIONS Results ACKNOWLEDGE AND CONTACT Zilouchian,Roshanak University of Illinois at Urbana-Champaign. Xiang, Dong, University of Illinois at Urbana-Champaign Nicholoson, Zane, University of Illinois at Urbana-Champaign University of Illinois at Urbana-Champaign, Champaign, IL Dong Xiang Application of Sentiment Analyses in Procid Project In Open Source Community, it’s quite often that someone made some modification to an existing feature, others will make comments below to support this modification or hold negative views about the changes. However, current Open-Source Issue Management Systems don’t provide explicit support for these kinds of collaborations. Procid is an interactive system supporting consensus building in open source issue management systems. In order to make the comments made by people becoming more and more positiveBy getting the position it locates, the web application can give positive or negative feedback to the user and suggest the user to give more positive feedback so that as time goes by, the open-source community can become better and better, which is what we want to see. The average score for positive comments is 0.11925176637789271. The standard deviation for positive comments is 0.00934734082404558 The average score for negative comments is - 0.10884571217103338 The standard deviation for negative comments is 0.009818107169926553 Flowchart to get the data distribution Data distribution for negative scores Screenshots for Drupal website Datadistribution for positive scores Screenshots when making comments via procid