Integration of Sentiment Analysis into Customer Relational Model: The Importance of Feature Ontology and Synonym

TLDR

Online commerce has led to a surge in customer product reviews, providing valuable insights for both buyers and companies. The study proposes a novel multi‑dimensional opinion‑mining model that integrates customer characteristics with their product opinions. The model extracts subjective expressions from reviews, stores them in a fact table, and represents them across customer, product, time, and location dimensions, with OLAP and data‑cube techniques used to analyze opinionated sentences and compute customer orientation on product features.

Abstract

Online business or Electronic Commerce (EC) is getting popular among customers today, as a result large number of product reviews have been posted online by the customers. This information is very valuable not only for prospective customers to make decision on buying product but also for companies to gather information of customers' satisfaction about their products. Opinion mining is used to capture customer reviews and separated this review into subjective expressions (sentiment word) and objective expressions (no sentiment word). This paper proposes a novel, multi-dimensional model for opinion mining, which integrates customers' characteristics and their opinion about any products. The model captures subjective expression from product reviews and transfers to fact table before representing in multi-dimensions named as customers, products, time and location. Data warehouse techniques such as OLAP and Data Cubes were used to analyze opinionated sentences. A comprehensive way to calculate customers' orientation on products' features and attributes are presented in this paper.