The helpfulness of online reviews depends on their textual portion. Using the information provided by the seller as a baseline, this study applies latent semantic analysis (LSA) to assess what parts of that textual portion contribute to helpfulness by separating the text into three categories of high entropy words: (1) unique (i.e. does not appear in previous reviews) corroboration entropy, (2) recommendation entropy, and (3) unique opinion entropy. Unique corroboration entropy is calculated based on the number of words in this review that describe the product on the seller’s site, confirming the seller’s claims, which did not appear in previous reviews. Recommendation entropy is based on the number of words that are associated with explicit recommendations. Unique opinion, referred as “regular opinions” in the literature, entropy is based on the number of all the other words in the review that provide positive or negative evaluations of products as well as other additional informational elements that did not appear in previous reviews. The results show that both recommendation and unique opinion entropies (only marginally) increase review helpfulness evaluations, while greater unique corroboration entropy is insignificant.

在线评论的帮助取决于其文本部分。使用卖方为基准所提供的信息，本研究采用潜在语义分析（LSA）来评估什么是文字部分的零件造成乐于助人由文本分成三类高熵的话：（1）独特的（即不以前的评论中未出现）确证熵，（2）推荐熵和（3）独特意见熵。根据此评论中描述卖方站点上产品的单词数，计算唯一的确证熵，从而确认卖方的主张，而先前的评论中没有出现。推荐熵是基于与显式推荐相关的单词数。独特的观点（在文献中称为“常规观点”），熵是基于评论中提供产品正面或负面评价的所有其他词语的数量以及以前评论中未出现的其他附加信息元素。结果表明，推荐和唯一意见熵（仅略微增加）都增加了评论有用性评估，而更大的唯一佐证熵却无关紧要。