# A Methodological Framework for AI-Driven Textual Data Analysis in Digital Media
## 3 The Proposed Approach: Data Analysis Framework for Information and Media
### Knowledge Discovery  
Knowledge discovery is an iterative process aimed at identifying non-obvious patterns, relationships, and trends within large datasets. This process employs data mining techniques, such as classification, clustering, and association algorithms, alongside statistical models and machine learning approaches. The use of specific techniques must align with the purposes of the research being conducted and the context of the data. In other words, there are certain types of problems where the application of clustering or classification solutions is not necessary. Data visualization--through graphs, tables, and other visual representations--plays a critical role in exploring and communicating findings. Figure 4 illustrates the proposed process flowchart, in which statistical analysis and predictive modeling are conducted in parallel and interactively. Subsequently, the results are consolidated using tabulation and data visualization techniques. The application of clustering algorithms, such as DBSCAN [65; 66; 67] and Deep Embedded Clustering (DEC) [68; 69], as well as text classification techniques like Random Forest [70; 71] and LLMs [72; 73; 74; 75], such as BERT and GPT, plays a central role in knowledge discovery for digital media analysis. These techniques enable the extraction of useful and relevant information from large volumes of textual data, uncovering latent patterns and grouping information to facilitate interpretations tailored to various research contexts. Following our proposed approach, models based on natural language processing (NLP) algorithms should be adapted to meet the specific needs of the research analyses, being selected and configured according to the identified research problem. For instance, sentiment analysis solutions, which measure public perception on specific topics [76; 77; 78], are particularly relevant for studies exploring public opinions. Conversely, methods focused on detecting fake news and clickbait [79; 80; 81; 82] and topic analysis [83; 84] are better suited for investigations aimed at identifying and understanding misleading content. By overcoming the limitations of manual analysis, these approaches allow for the identification of emerging trends, audience segmentation, and the evaluation of communication strategies. However, it is crucial to acknowledge the limitations of these techniques, such as their challenges in capturing linguistic nuances, ambiguities, and the influence of contextual factors. These challenges can be mitigated through advancements in deep learning, particularly with the use of pre-trained language models, which offer enhanced contextual and semantic interpretation capabilities in textual analysis [85; 86]. Additionally, the application of computational solutions must be guided by best practices in contextualization, testing, and validation of the models employed. In conjunction, the integration of statistical techniques is employed to enhance the robustness of the analyses. Quantitative models enable the measurement of the significance of identified patterns, the validation of hypotheses, and the assurance that the inferences drawn are replicable. This combination of artificial intelligence and statistical methods facilitates the transformation of raw data into comprehensible indicators, providing analytical foundations both for the outcomes derived during the knowledge discovery stage and for the variables previously generated during the data enrichment phase. The final stage of the process involves data visualization, which translates results into intuitive representations. In the context of NLP solutions, specific visualizations, such as similarity graphs for analyzing semantic relationships and word clouds for identifying the most frequent terms, highlight relevant textual patterns. Word clouds are visual representations that reflect the frequency of terms in a textual corpus, where terms are displayed in sizes proportional to their frequency.