Feedback from the users of software products is vital for engineering better requirements. One tool for generating requirements from online user feedback is clustering, where the most mentioned topics by users are found by grouping alike feedback together. These clusters have been summarized in previous work using characterizing phrases or sentences for these topics to be understood. This work evaluates which method of characterization (unigrams, bigrams, trigrams, sentences) is most effective for understanding the semantic meaning of a whole cluster using feedback from multiple feedback sources. We evaluate multiple characterization methods against random characterizations to determine the ability of each method to create distinct, descriptive characterizations. We further evaluate the amount of requirements relevant characterizations created by each characterization method. We find that unigrams, bigrams, trigrams, and full sentences all perform similarly in distinguishing clusters in the random characterization comparison task. However, we find that fewer and more expressive characterizations, such as full sentences, contain more requirements relevant information from a feedback cluster compared to more numerous but less expressive unigrams. Our findings inform the future development of user feedback clustering tools, with different cluster characterization methods being quantitatively measured for the first time.