This paper proposes fast and small-area FeFET-based voltage-sensing analog Computation-in-Memory (CiM) for hyperdimensional computing (HDC) by eliminating large-scale digital circuit overhead. In both training and inference of HDC, MAP (bit-wise XOR, bit-wise majority rule, and 1-bit shift) of hypervectors (HVs) is operated by Partially added Text HV FeFET CiM and Text HV FeFET. In inference, Similarity search FeFET CiM obtains classification result. By taking an example of Language classification problem, the proposed voltage-sensing FeFET CiM for HDC encodes HV in training by 5,000 times faster and smaller area than the conventional method.