This paper presents a method for using topic distributions generated from topic models as features for performing sentiment analysis on documents. This will be tested in the social media domain, specifically Twitter. The proposed approach allows for the mapping from word space to topic space which allows for less features to be needed and also reduces computational complexity. Multiple machine learning algorithms will be used to test the topic model generated features and a number of different versions of test corpus will be used, including unigrams, bigrams, part-of-speech tagging and adjectives only. The method proposed will also be compared to other notable topic-sentiment methods such as the aspect-sentiment unification model and the joint sentiment/topic model. The results show that using topic distributions can improve the accuracy of classification algorithms, however, the performance can be dependent on the algorithm used and the initial features used. Additionally, we show that using only topics as features outperforms the hybrid topic-sentiment models.