Ontology Driven Feature Engineering for Opinion Mining

Abstract

In the process of knowledge discovery, the reliability of results depends upon the effectiveness of attributes selected for decision. The curse of dimensionality refers to the phenomenon in which the excessive number of dimensions affect the analysis. In order to eradicate the curse of dimensionality in text analysis, we are proposing an ontology-based semantic measure for intelligent selection/reduction of features. Among the various text mining techniques, ontology-based mining has a significant contribution to the field. The ontology-based semantic measures, which are mathematical models used to find the similarity between various concepts in the ontology, have made a significant contribution to feature engineering. The proposed measure is an amalgamation of semantic similarity, relatedness, and distance. The measure allows performing an in-depth analysis of various semantic relationships between concepts of the English language. The performance of the measure was evaluated against benchmarked dimension reduction techniques such as PCA. The results show improvement by reducing the size of dimensions up to 35%. The results were further evaluated by training a classifier to validate that the features are not creating any underfit/overfit model. INDEX TERMSFeature engineering, dimension reduction, semantic measures, ontology.

Ontology-Based Feature Selection: A Survey

“…An example of such a difficulty is related to the fact that positive (negative) document on an object does not imply that the user has positive (negative) opinion regarding the whole set of features assigned to that document [80,81]. The ontology-based feature selection for sentiment analysis is a complex and difficult endeavor, mainly because it involves high semantic representations of expressed opinions along with diversified characteristics encoded in the ontology as well as in the corresponding features [78,80,83].…”

“…The second level carries out the implementation of a domain ontology to identify the most important features included in initial set of features. The domain ontology can be generic [80,83] or custom (i.e., created for a specific application) [77,82,83]. As seen in Figure 3, the input to this step is the pre-processed corpus of opinions as well as the domain ontology, while its output comes in the form of potential features identified from the text, which are then represented in some convenient form (e.g., vector-based representation, etc.…”

“…As seen in Figure 3, the input to this step is the pre-processed corpus of opinions as well as the domain ontology, while its output comes in the form of potential features identified from the text, which are then represented in some convenient form (e.g., vector-based representation, etc. ), which will help their elaboration by the next levels [80,83]. As far as the type of ontology is concerned, it can be a crisp ontology i.e., a precise (binary) specification of a conceptualization [79,80,[83][84][85] or ontology based on fuzzy set theory [86,87].…”

Exploiting Ontology Recommendation Using Text Categorization Approach

“…Ontologies can be used in many research areas to support a wide range of tasks such as natural language processing, knowledge representation, information retrieval, databases, online database integration, knowledge management, visual information retrieval, geographic information systems, digital libraries, or multi-agent systems [56]. Furthermore, many researchers are using the ontology related systems in different fields such as Diagnostics [57], Recommendation and classification [58], [59], IoT security [60], content analysis [61] and opinion mining [62]. However, considering the ontology reuse as a defined design pattern, little or no attention is being paid to the reuse of existing ontologies to reduce the costs [10].…”

Framework for opinion as a service on review data of customer using semantics based analytics

“…Study towards opinion mining over the social network is carried out by Riquelme et al [11]. Existing system was also recorded to use ontology-based approach for carrying out opinion mining as seen in the work of Siddiqui et al [12]. The study has also used semantics using mathematical modeling towards facilitating feature engineering.…”