This study proposes a novel two-phase flow control technique by integrating fuzzy inference algorithms and supervisory commands sentiment analysis. The innovation of the proposed scheme lies in its two-feedback loop mechanism, whereby the predicted flow type is compared not only with the actual flow generated but also with the order sentiment. This renders the control more intuitive and responsive, introducing user-friendliness and system efficiency. Integrating voice commands and emotional evaluation introduces an additional dimension to human-machine interaction, enhancing performance in general. The study demonstrated that sentiment-based fuzzy logic boosts adaptability in control by enabling the system to respond effectively to dynamically varying conditions. Integrating fuzzy inference, sentiment analysis, and voice command recognition introduces a degree of working flexibility whilst circumventing the limitations of conventional fuzzy controllers, such as manual tuning complexity. The experimental results confirmed the proposed system's stability and accuracy in handling uncertain or vague commands, thereby ensuring smooth control performance. The study identified key advantages, including enhanced user convenience, streamlined decision-making processes, and improved responsiveness to operator intent. However, it is essential to note that potential issues, such as misinterpreting commands due to environmental noise or ambiguous wordings, may arise. Nevertheless, the model is designed to prevent erroneous settings and false propagation of commands by clearly separating the model from the external environment. The study confirms that sentiment-augmented fuzzy logic control is a viable solution to intelligent and adaptive two-phase flow control. Future studies should aim to refine sentiment interpretation and broaden the system's applicability to other industrial processes.