Iranian Journal of Information Management 1735-8418 8 2 2023 02 20 Proposing A Method For Analyzing User Comments On Twitter Social Media Platform For Business Model Innovation Proposing A Method For Analyzing User Comments On Twitter Social Media Platform For Business Model Innovation 202 220 197450 10.22034/aimj.2023.197450 FA Monireh Hosseini Associate Prof., Department of Information Technology Engineering, Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran 0000-0002-3677-0811 Tina Akhlaghi Meskin MSc. Student, Department of Information Technology Engineering ,Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran Journal Article 2023 08 31 In our current era, with the advancements in the use of Internet, a large amount of textual data is being rapidly created. Words are an inseparable part of online transactions in E-commerce. Online comments, news, marketing and other types of communication along with the digitalization of data creates a huge amount of textual data that businesses would like to use. Textual data gets created by people, organizations and communities. In the past, researchers and businesses employed manual methods to gain insights about their customers, which required significant human effort, financial resources, and processing time. Due to human intervention in analysis, these methods were susceptible to analyst and respondent biases. By using text mining we can analyze the necessary information for companies in an automatic and cost-effective way in real time, enabling the use of the results for business model innovation. This research examines customer tweets directed at the Apple company and the company's responses using sentiment analysis methods and Latent Dirichlet Allocation. Furthermore, a new method has been suggested to combine the results of these two methods in order to gain deeper insights into the data and use the results to give recommendations for enhancing Apple company’s business model. In our current era, with the advancements in the use of Internet, a large amount of textual data is being rapidly created. Words are an inseparable part of online transactions in E-commerce. Online comments, news, marketing and other types of communication along with the digitalization of data creates a huge amount of textual data that businesses would like to use. Textual data gets created by people, organizations and communities. In the past, researchers and businesses employed manual methods to gain insights about their customers, which required significant human effort, financial resources, and processing time. Due to human intervention in analysis, these methods were susceptible to analyst and respondent biases. By using text mining we can analyze the necessary information for companies in an automatic and cost-effective way in real time, enabling the use of the results for business model innovation. This research examines customer tweets directed at the Apple company and the company's responses using sentiment analysis methods and Latent Dirichlet Allocation. Furthermore, a new method has been suggested to combine the results of these two methods in order to gain deeper insights into the data and use the results to give recommendations for enhancing Apple company’s business model. https://www.aimj.ir/article_197450_fc49424986f207efaad892059dcd5eb0.pdf