ASPECT IDENTIFICATION USING TOPIC MODELING FOR DRUG REVIEWS

AbstractNow a days in the world of internet, many studies have observed that patients are getting importantand supporting information on persistent diseases and related drugs from resources such as onlinereviews, web blogs, and discussion forums. Mining of information from such bulk of text is achallenging task but this information is very useful. Such information can be processed using the modelcalled Aspect Identification using Topic Modeling (AITM) on Drug reviews which is the proposedsystem. This model recognizes the aspects or topics associated with the class label of the mixture ofdata. The important characteristic of Aspect Identification using Topic Modeling on Drug Reviews isthat it determines aspect relevant to one class only. So, the chances of getting aspects of differentconcepts from different classes are suppressed and resulting aspects can be easily interpreted. Theresulting aspects are class distinguishing means they are helpful in distinguishing a class from other.The coherent Latent Dirichlet Allocation (LDA) topic modeling algorithm is used for result extraction.Four different drugs (i.e. Citalopram, Escitalopram, Lisinopril and Simvastatin) are used for analyzingexperimental results of the proposed system. The analysis shows that AITM is giving betterperformance than other existing approaches. AITM has improved accuracy up to 98.3% compared toexisting system, also the numbers of aspects identified by AITM are more than the existing system. Keywords: Drug review, Text mining, Aspect mining, Topic modeling.