Strategic planning (SP) requires attention and constant updating and is a crucial process for guaranteeing the efficient performance of companies. This article proposes a novel approach applied in a case study whereby a balanced scorecard (BSC) was generated that integrated sentiment analysis (SA) of social media (SM) and took advantage of the valuable knowledge of these sources. In this study, opinions were consolidated in the main dataset to incorporate sentiments regarding the strategic part of a restaurant in a tourist city. The proposed methodology began with the selection of the company. Information was then acquired to apply pre-processing, processing, evaluation, and validation that is capitalized in a BSC to support strategic decision-making. Python support was used in the model and comprised lexicon and machine learning approaches for the SA. The significant knowledge in the comments was automatically oriented toward the key performance indicators (KPIs) and perspectives of a BSC that were previously determined by a group of opinion leaders of the company. The methods, techniques, and algorithms of SA and SP showed that unstructured textual information can be processed and capitalized efficiently for optimal management and decision-making. The results revealed an improvement (reduced effort and time) to produce a more robust and comprehensive BSC with the support and validation of experts. Moreover, new resources and approaches were developed to implement more efficient SP. The model was based on the efficient coupling of both fields of study.

Purpose: Strategic planning (SP) enables enterprises to plan management and operations activities efficiently in the medium and large term. During its implementation, many processes and methods are manually applied and may be time consuming. The purpose of this paper is to introduce an automatic method to define strategic plans by using text mining (TM) algorithms within a generic SP model especially suited for small- and medium-sized enterprises (SMEs). Design/methodology/approach: Textual feedbacks were collected through a SWOT matrix during the implementation of a SP model in a company dedicated to the local distribution of food. A four-step TM process (performing acquisition, pre-processing, processing, and validation tasks) is applied via a framework developed under the cloud computer paradigm in order to determine the strategic plans. Findings: The use of categorization and clustering algorithms show that unstructured textual information produced during the SP can be efficiently processed and capitalized. Collected evidence reveals the potential to enhance the strategic plans creation with less effort and time, improving the relevance, and producing new technological resources accessible to SMEs. Originality/value: An innovative framework especially suited for the SMEs based on the synergy assumption of the coupling between TM and a generic SP model.