In the realm of public health, the discourse surrounding diseases significantly impacts awareness, funding, and stigma. Traditional methods of analyzing disease mentions often overlook the intricate relationships and sentiments expressed in text, which can provide deeper insights into public perception and information dissemination. Existing literature on disease discourse primarily focuses on quantitative metrics such as disease frequency counts or associations based on semantic similarity. However, such analyses rarely account for the sentiment of the discourse, which can play a critical role in shaping public perception and response to health information. Moreover, the complexity of textual data and the subtleties of language usage present significant challenges for extracting meaningful patterns, especially when considering the emotional context of disease-related discussions. This study introduces a novel approach that integrates sentiment analysis and network visualization to examine the discourse on diseases within a comprehensive textual dataset. By applying the TextBlob library, we analyze sentence-level sentiment and categorize it as positive or negative. Utilizing these sentiment scores, we construct two distinct network graphs to depict the relationships between diseases based on their co-occurrence within sentiment-laden sentences. The analysis reveals contrasting landscapes of disease discourse: the positive sentiment network highlights diseases frequently mentioned in the context of successful treatment or optimistic outcomes, forming distinct clusters around well-managed conditions. In contrast, the negative sentiment network elucidates diseases that coalesce around shared public concerns, fears, or complications, marking out critical areas for public health intervention. The findings underscore the importance of sentiment in understanding disease discourse, offering a novel perspective that can assist public health officials in tailoring communication strategies. By recognizing patterns in disease associations within negative contexts, interventions can be more effectively targeted to address misconceptions and alleviate public health anxieties. Conversely, identifying diseases with strong positive connections can guide campaigns to bolster preventive measures and highlight medical advancements. This methodology serves as a blueprint for leveraging sentiment analysis in the exploration of health-related textual data, paving the way for more nuanced and actionable insights into public health narratives.