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The research aims to provide a longitudinal case study to understand how digital transformation can be embedded in municipal reporting frameworks. The central role of such technology becomes increasingly evident as citizens demand greater transparency and engagement between them and governing institutions.

Utilising a longitudinal case study methodology, the research focusses on Turin’s Integrated Popular Financial Report (IPFR) as a lens through which to evaluate the broader implications of digital transformation on governmental transparency and operational efficiency.

Digital tools, notably sentiment analysis, offer promising avenues for enhancing governmental efficacy and citizenry participation. However, persistent challenges highlight the inadequacy of traditional, inflexible reporting structures to cater to dynamic informational demands.

Embracing digital tools is an imperative for contemporary public administrators, promoting streamlined communication and dismantling bureaucratic obstructions, all while catering to the evolving demands of an informed citizenry.

Different from previous studies that primarily emphasised technology’s role within budgeting, this research uniquely positions itself by spotlighting the transformative implications of digital tools during the reporting phase. It champions the profound value of fostering bottom-up dialogues, heralding a paradigmatic shift towards co-creative public management dynamics.

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.