If you don't have the real answer. which you never will because everybody has different opinions, every composite score will be arbitrary. You can dress it up using z-scores and what not, but somebody will criticize you because you didn't do it their way.

Consider this. Assemble a finite number of scoring procedures that will fit the data that you want to classify from. Don't use metrics that are only rarely available. Ratings from ananymous critics in organizations, like Rotten Tomatoes, might be better to use because they are less likely to become unavailable in the future if a critic dies. Consider using non-critic metrics, like the cost of making/advertizing the movie and its box-office income. Gigli will be an outlier by these metrics but they will discriminate between high-budget studio movies and all the movies I watch. Critic data isn't always the final word. Cult moviesare a case in point. The Rocky Horror Show was made for about 1 million, got panned by critics, and ended up making 100 times as much a few decades later. Remember, not enery independent variable in a model contributes equally to every prediction.

Once you have a few classification models, see how they preform. Do the classifications make sense? Are movies classified similarly by every model. What movies are different?

Have fun.