AN OPINION AND CONTEXT-AWARE INVERTED INDEX GRAPH MODEL FOR SENTIMENT SEARCH AND ANALYSIS

Two major functions required in textual data modelling and knowledge discovery especially in modern customer interaction and research are search and sentiment analysis. Recent research has however not been able to combine these two in a way that instils flexibility into the iterative process of textual analysis for decision making. In this work, the Opinion and Context-Aware Inverted Index graph model is proposed for performing sentiment search and analysis on textual data. Unlike previous works, the use of the proposed model retrieves needed information without requiring re-processing of sentiment information. The proposed approach clarifies the difference between sentiment search and sentiment categorization. Furthermore, it shows how to perform opinion aware search based on graph traversals, pattern matching and aspect opinion filtering. To evaluate the flexibility and capabilities of the proposed scheme, the feature interest range, customer feature satisfaction/dissatisfaction intensity, feature co-mentions, and sentiment based ranking metrics of various bipartite projections for feature co-mention sub graphs using PageRank are extracted from a well-studied dataset on online product reviews with an extension of the proposed model. The chosen design and methodology is shown to be suitable for big data scale implementations.