Natural resources exist independent of human intervention. Although these interventions can and do affect the balance between ecological and biological diversity conditions these resources support, and their use to promote economic development. Currently, the unsustainable use of these resources threatens this balance, calling for more sustainable patterns of natural resource use and conservation. The primary responsibility for ensuring the proper balance lies with governments, leading to various policies and programs to preserve natural resources. The ultimate goal is to make the masses aware of natural assets’ importance and encourage their sustainable use. To successfully implement, however, these government practices require public communication and participation, and the full consideration of public opinion at various levels of governance. A predictive analytics framework is proposed for understanding public opinion on government policies to improve sustainable water governance. An integrated policy initiative to balance water resources use and conservation launched by the Indian government served as a test case for applying the framework in an attempt to accurately classify the opinion polarity related to the policy. The conventional feature extraction is applied to pre-processed datasets to extract the relevant features. Subsequently, swarm-based feature selection is applied to filter out optimal features. Lastly, opinion mining and textual analysis are performed to determine the most relevant water management factors that need immediate attention. The proposed framework serves as a policy evaluation strategy in the water management domain. The paper closes with a discussion of the general applicability of the proposed framework.

自然资源独立于人类干预而存在。尽管这些干预措施可以而且确实会影响这些资源支持的生态和生物多样性条件之间的平衡，并利用它们来促进经济发展。目前，这些资源的不可持续使用威胁着这种平衡，需要更可持续的自然资源使用和保护模式。确保适当平衡的主要责任在于政府，从而制定各种保护自然资源的政策和计划。最终目标是让大众意识到自然资产的重要性并鼓励其可持续利用。然而，要成功实施这些政府做法，需要公众的沟通和参与，以及在各级治理中充分考虑民意。提出了一个预测分析框架，以了解公众对政府政策的看法，以改善可持续的水治理。印度政府发起的平衡水资源利用和保护的综合政策举措作为应用该框架的测试案例，试图准确地对与政策相关的意见极性进行分类。常规特征提取应用于预处理数据集以提取相关特征。随后，应用基于群的特征选择来过滤掉最优特征。最后，进行意见挖掘和文本分析，以确定需要立即关注的最相关的水管理因素。提议的框架作为水资源管理领域的政策评估策略。