This paper reports the efficiency and effectiveness of our proposed text analysis module for Web-site Trust Assessment System. One main factor which influences the level of trust for e-commerce Web site is the textual content appearing on each Web page especially the main page of the Web site. The textual content refers to words, phrases and sentences which are shown on the page. A Web site which has high trust-level should contain meaningful keywords related to e-commerce domain such as return policy, payment option and security. To analyze the textual content, our text analysis module adopts automatic classification techniques to learn from the example Web-site data set. We perform experiments on two e-commerce domains, jewelry and book shop, by using three well-known classification algorithms. A sample set of Web sites under each domain are collected and labeled as trust or untrust for performing our experiments. Two approaches for constructing the feature set are (1) using all extracted words from the textual contents (baseline) and (2) by mapping extracted words into the meaningful groups of e-commerce terminology (EC-word). The best text analysis result of 83.5 % accuracy was obtained when the Support Vector Machines based on Sequential Minimal Optimization (SMO) algorithm was applied with the EC-word feature set.