Priority Based Sentiment Analysis for Quick Response to Citizen Complaints

Today metropolitan citizens need a common platform to register their complaints. In the traditional telephonic system task of complaint registration is very time-consuming process so, citizen have to wait until call is received by the service executive. The proposed framework is developed for Pune Municipal corporation (PMC) will be very helpful for registering queries in natural language and get immediate response. Understanding the short text is the main challenge of the system as short text do not follow syntax of the written language, short text does not have sufficient statistics to support approach of text mining, short text is noisy and ambiguous. So, traditional Part-of-Speech (POS) tagging tools cannot be easily applied. In proposed framework for understanding natural language semantic knowledge provided by well-known knowledgebase WordNet is used. In prequery citizens inserts complaint to system and get immediate response to query with the help of knowledgebase and machine learning algorithm. In postquery system analyses the citizen sentiment to handle grievance level and accordingly prioritize the citizens by sentiment analysis. The proposed framework will help many organizations to ensure quality service provision and customer satisfaction with less human efforts.