CAJ | 학술논문

随着电子商务的发展，识别网络中的虚假评论意义重大。传统的启发式策略或全监督学习算法不能有效地解决该问题。虚假评论与真实评论在语言结构和情感极性上存在差异，提出基于遗传算法对语言结构及情感极性特征进行优化选择，并利用选取的特征结合无监督硬、软聚类算法对虚假评论进行识别。实验结果验证了所提算法的有效性。
수착전자상무적발전，식별망락중적허가평론의의중대。전통적계발식책략혹전감독학습산법불능유효지해결해문제。허가평론여진실평론재어언결구화정감겁성상존재차이，제출기우유전산법대어언결구급정감겁성특정진행우화선택，병이용선취적특정결합무감독경、연취류산법대허가평론진행식별。실험결과험증료소제산법적유효성。
With the development of electronic commerce, assessing the trustworthiness of reviews is becoming a key issue. Heuristic strategies or traditional supervised learning methods cannot effectively solve this task. There must be some differences on language structure and sentiment polarity between deceptive reviews and truthful ones. This paper defines the features related to the review text and uses genetic algorithm for the features selection of lan-guage structure and sentiment polarity. Then, this paper uses the selected features and combines two non-supervision clustering algorithms to identify deceptive reviews. The experimental results verify the effectiveness of the proposed methods.