Public opinion mining using natural language processing technique for improvisation towards smart city

• Published in: International journal of speech technology Vol. 24; no. 3; pp. 561 - 569
• Main Authors: Leelavathy, S., Nithya, M.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2021; Springer Nature B.V
• Subjects: Ambulances; Applications programs; Artificial Intelligence; COVID-19; Data mining; Electronic government; Engineering; Feedback; Fuzzy logic; Machine learning; Markov analysis; Markov chains; Messages; Mobile computing; Natural language processing; Opacity; Pandemics; Patients; Public opinion; Sentiment analysis; Signal,Image and Speech Processing; Smart cities; Social Sciences; Voice; Fuzzy logic; Covid 19; Natural language processing; Speech processing; Hidden Markov models
• ISSN: 1381-2416; 1572-8110
• DOI: 10.1007/s10772-020-09766-z

In this digital world integrating smart city concepts, there is a tremendous scope and need for e-governance applications. Now people analyze the opinion of others before purchasing any product, hotel booking, stepping onto restaurants etc. and the respective user share their experience as a feedback towards the service. But there is no e-governance platform to obtain public opinion grievances towards covid19, government new laws, policies etc. With the growing availability and emergence of opinion rich information’s, new opportunities and challenges might arise in developing a technology for mining the huge set of public messages, opinions and alert the respective departments to take necessary actions and also nearby ambulances if its related to covid-19. To overcome this pandemic situation a natural language processing based efficient e-governance platform is demandful to detect the corona positive patients and provide transparency on the covid count and also alert the respective health ministry and nearby ambulance based on the user voice inputs. To convert the public voice messages into text, we used Hidden Markov Models (HMMs). To identify respective government department responsible for the respective user voice input, we perform pre-processing, part of speech, unigram, bigram, trigram analysis and fuzzy logic (machine learning technique). After identifying the responsible department, we perform 2 methods, (1) Automatic alert e-mail and message to the government departmental officials and nearby ambulance or covid camp if the user input is related to covis19. (2) Ticketing system for public and government officials monitoring. For experimental results, we used Java based web and mobile application to execute the proposed methodology. Integration of HMM, Fuzzy logic provides promising results.