Multilingual corpora are widely exploited in several tasks of natural language processing, these corpora are principally of two sorts: comparable and parallel corpora. The comparable corpora gather texts in several languages dealing with analogous subjects but are not translations of each other such as in parallel corpora. In this paper, a comparative study on two stemming techniques is conducted in order to improve the comparability measure based on a bilingual dictionary. These methods are: Buckwalter Arabic Morphological Analyzer (BAMA) and a proposed approach based on Light Stemming (LS) adapted specifically to Twitter, then we combined them. We evaluated and compared these techniques on three different (English-Arabic) corpora: a corpus extracted from the social network Twit-ter, Euronews and a parallel corpus extracted from newspapers (ANN). The experimental results show that the best comparability measure is achieved for the combination of BAMA with LS which leads to a similarity of 61% for Twitter, 52% for Euronews and 65% for ANN. For a confidence of 40% we aligned 73.8% of Arabic and English tweets.