An entity based LDA for generating sentiment enhanced business and customer profiles from online reviews

The accelerated growth of the Web2.0 has led to an abundance of accessible information which has been successfully harnessed by many researchers for personalizing products and services. Many personalization algorithms are focused on analyzing only the explicitly provided information and this limits the scope for a deeper understanding of the individuals’ preferences. However, analyzing the reviews posted by the users seeks to provide a better understanding of users’ personal preferences and also aids in uncovering business’ strengths and weaknesses as perceived by the users. Topic Modeling, a popular machine learning technique addresses this issue by extracting the underlying abstract topics in the textual data. In this study, we present entity-LDA (eLDA), a variation of Latent Dirichlet Allocation for topic modeling along with a dependency tree based aspect level sentiment analysis methodology for constructing user and business profiles. We conduct several experiments for evaluating the quantitative and qualitative performance of our proposed model compared to state-of-the-art methods. Experimental results demonstrate the efficacy of our proposed method both in terms topic quality and interpretability. Finally we develop a framework for constructing user and business profiles from the topic probabilities. Further we enhance the business profiles by extracting syntactic aspect level sentiments to indicate sentimental polarity for each aspects.