Aspect-level multimodal sentiment analysis involves analyzing sentiment and opinions at the aspect or attribute level. Previous studies on image-text multimodal sentiment analysis have explored various methods for extracting and fusing features from images and text. Since the initial semantic spaces of images and text are not aligned, common approaches extract deep features from each modality, map them to a shared deep semantic space, and then apply a fusion module. However, this inevitably increases model complexity. With advancements in large language models, aligning the semantic spaces of images and text at a shallow level has become feasible. This study leverages Qwen to generate textual descriptions of images through prompt engineering during preprocessing, transforming multimodal sentiment analysis into a unimodal sentiment analysis task. This enables sentiment analysis results to be obtained using only a text processing module. Experimental results show that, compared to most previous models, the proposed method significantly reduces the number of parameters while achieving performance improvements. Compared to the similarly lightweight TISRI model, it also demonstrates notable advantages in training speed and resource utilization. The code is available at: https://github.com/triangleXIV/ITFFT.