The helpfulness of online reviews and their impact on purchase decisions is well established. Much previous research measured that helpfulness by analyzing vote assessments. This study examines an alternative semantic measure based on a text analysis of the term helpful in those reviews. Analyzing over 20,000 reviews shows that the semantic measure has a considerably higher R-2 than vote assessments. Moreover, the new measure, as opposed to those based on votes, is not affected by posting order, avoiding a known source of bias in vote measures, and is conceptually unrelated to the number of previous helpfulness evaluations. The study also examines the role of the incremental entropy of each review's content as a new determinant of both the existing measures and the new semantic measure of online review helpfulness. The potential of the semantic measure, including that it can be automatically calculated even before human review users read the review, is discussed.