An effective approach to finding a context path in review texts using pathfinder scaling

Abstract

Customer reviews feature opinions or sentiments that a review writer has given, and these opinions or sentiments have an impact on the reader. Identifying and presenting word associations that indicate a sentiment orientation and semantics can aid in selecting the best review for providing the information customers are seeking. In this paper, we attempted to discover the context structure and the context path presenting explicit semantics in review texts. To this end, we extracted word co-occurrences and converted them to a cosine adjacency matrix. Then a co-word network applied by Pathfinder scaling was constructed. Finally, we measured the context score and presented context paths from the context structure in the review texts. In results, our approach found that a compound noun is easy to detect by network analysis. The extracted context paths remain intact, a sentiment polarity derived from review texts. The evaluative expression for a certain aspect of a product or service is clearer and more specified within the context path. Furthermore, it is not necessary to train reference words to detect the sentiment orientations.