Analyzing Employee Voice Using Real-Time Feedback

People nowadays tend to use social media as a platform to share their reviews, emotions, and opinions, including about their jobs. Thus, a lot of data is available on the web. Therefore, a rapid response is needed to analyze and interpret the data. Unfortunately, many organizations still use annual surveys to assess satisfaction, engagement, and culture in the workplace. Compared to other conventional datasets such as company survey and questionnaire, decision-makers could make decision effectively and efficiently by using the interpreted data. This may be done with the help of sentiment analysis method. In this research, we classify the feedback based on its category and sentiment. Several classification algorithms are used in opinion mining; two of them are Naive Bayes Classifier (NBC) and Support Vector Machine (SVM). This paper aims to classify feedback based on sentiments using NBC and SVM.