Innovative Business Process Reengineering Adoption: Framework of Big Data Sentiment, Improving Customers’ Service Level Agreement

• Published in: Big data and cognitive computing Vol. 6; no. 4; p. 151
• Main Authors: Susanto, Heru, Sari, Aida, Leu, Fang-Yie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2022
• Subjects: Big Data; Business process reengineering; classification; clustering; Communications industry; Computational linguistics; Consumers; Customer feedback; Customer satisfaction; Customer services; Customers; Data mining; Digital media; information extraction; information retrieval; Internet; Language processing; Literature reviews; Machine learning; Marketing; Natural language interfaces; Reengineering; Sentiment analysis; Social media; Social networks; Supervised learning; Support vector machines; Telecommunications industry; Telecommunications services industry; text analysis; Tourism; Trend analysis; visualization; United Kingdom
• ISSN: 2504-2289; 2504-2289
• DOI: 10.3390/bdcc6040151

Social media is now regarded as the most valuable source of data for trend analysis and innovative business process reengineering preferences. Data made accessible through social media can be utilized for a variety of purposes, such as by an entrepreneur who wants to learn more about the market they intend to enter and uncover their consumers’ requirements before launching their new products or services. Sentiment analysis and text mining of telecommunication businesses via social media posts and comments are the subject of this study. A proposed framework will be utilized as a guideline, and it will be tested for sentiment analysis. Lexicon-based sentiment categorization is used as a model training dataset for a supervised machine learning support vector machine. The result is very promising. The accuracy and the quantity of the true sentiments it can detect are compared. This result signifies the usefulness of text mining and sentiment analysis on social media data, while the use of machine learning classifiers for predicting sentiment orientation provides a useful tool for operations and marketing departments. The availability of large amounts of data in this digitally active society is advantageous for sectors such as the telecommunication industry. These companies can be two steps ahead with their strategy and develop a more cohesive company that can make customers happier and mitigate problems easily with the use of text mining and sentiment analysis for further adopting innovative business process reengineering for service improvements within the telecommunications industry.