Twitter is one of the most popular social media outlets available and has been expanding over the years in both scope and reach. The growing number of users, and the accessibility to their micro-posts and metadata make Twitter a popular subject for research in various research communities. In this paper, I propose and examine a content analysis method that utilizes the hierarchy of effects model, which has a very long history of use by both practitioners and academics in the field of advertising and marketing. I had judges manually annotate tweets in accordance with one of the five attitude stages the authors are assumed to be in: Atten-tion, Interest, Desire, Action, or Satisfaction. Next, I examine the tagged corpus from various aspects to iden-tify general traits and explore the possibilities for the newly gained information from the tweets. The results show that the consumer attitude information can improve the prediction quality of box-office revenues and may be used to better represent movie audience sentiment. These findings should aid other content analysis methods that utilizes Twitter by providing an additional dimension for the researchers to consider.