Evaluation of Korean Reviews Automatically Generated using Long Short-Term Memory Unit

Youngkyo Oh, Dongyoung Koo

http://doi.org/10.5626/JOK.2019.46.6.515

Opinion Spam is the act of misleading the public to comply with the intent of a particular group. This is a serious problem at this point in time, when online services are widely deployed and the reputation of a specific target seller relative to the offline market can be easily affected. In this context, detecting and preventing opinion spam is an important research topic. This study investigates the current status of the domestic online market and poses possible damages caused by opinion spam through the automatic generation of Korean reviews using a recurrent neural network (RNN). In particular, we applied a learning model based on an LSTM (long short-term memory) unit for the Korean language in order to improve the long-term dependency problem of the typical RNN. Then, the Word2Vec is exploited to improve the relevance by replacing keywords with a set of candidates from the target market. We show the translation of the learning model based on a foreign language to Korean, then evaluate its appropriateness with regard to the development of countermeasures with which to effectively prevent automatically generated opinion spam in the near future.