# Ontology-Based Feature Selection: A Survey
## 5 Ontology-Based Feature Selection
### Opinion Mining
The number of occurrences of \(f_{i}\) in those three text parts are defined as \(|O_{aj}|_{i}\), \(|O_{bj}|_{i}\), and \(|O_{cj}|_{i}\). By defining the respective importance degrees of occurrence of \(f_{i}\) in the above three text parts as \(z_{aj^{\prime}}^{i}\), \(z_{b}^{i}\), and \(z_{cj^{\prime}}^{i}\), the resulting score function reads as follows,  
\[score(f_{i},t_{j})=z_{aj}^{i}|O_{aj}|_{i}+z_{bj}^{i}|O_{bj}|_{i}+z_{cj}^{i}|O_{ cj}|_{i} \tag{12}\]  
The features are grouped in accordance with score value and attached to a main concept of the ontology. The set of the above concepts constitute the final set of features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Related Work** & **Ontology** &
\begin{tabular}{c} **Type of** \\ **Ontology** \\ \end{tabular} & **Classifier** \\ \hline
[80] & Movie Ontology & Crisp & Lexicon-based \\ \hline
[83] & Movie Ontology, WordNet & Crisp & SVM \\ \hline
[77] &
\begin{tabular}{c} Custom \\ based on FCA and OWL \\ \end{tabular} & Crisp & SVM \\ \hline
[85] & Movie Ontology & Crisp & Lexicon-based \\ \hline
[86] &
\begin{tabular}{c} Custom \\ based on fuzzy set theory \\ \end{tabular} & Fuzzy & SVM \\ \hline
[82] & Custom & Crisp & Lexicon-based \\ \hline
[86] & Custom & Fuzzy & Lexicon-based \\ \hline
[84] & Custom & Crisp & Lexicon-based \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Related works organized according to specific criteria. Finally, the polarity identification is carried out in terms of the SentiWordNet framework, where three possible outcomes are obtained namely, positive, neutral, and negative opinion. [PERSON] et al. [83] used standard NLP techniques to identify several potential features, which are added onto a feature vector. Then, a semantic processing approach takes place to select the most important features. The semantic processing is conducted in a sequence of steps. The first step applies a lexical pruning algorithm aiming to discard all features that are not part of lexical categories of the WordNet ontology. Second, they combined the Movie Ontology [88] and the standard WordNet ontology and developed a semantic similarity-based approach, that consists of three pairwise similarity measures namely the semantic similarity measure, the semantic relatedness measure, and semantic distance measure. Third, the calculated values of the above-mentioned pairwise similarity measures are gathered in a table that reports all the possible pairs. This table assists the computation of the weights of importance of each feature in relation to the rest of the features. To this end, an iterative algorithm is developed, which gradually refines the initial set of features, until the most important features are identified. The above algorithm is based on defining an appropriate threshold value, and the importance of a feature is decided according to whether the respective overall weight of importance is greater than the above threshold or not. Finally, the polarity identification is carried out in terms of a binary classification approach (i.e., positive or negative polarity) using a support vector machine algorithm. [PERSON] and [PERSON] [77] developed an ontology in OWL using formal concept analysis (FCA). The algorithmic framework attempts to form semantic structures that are formal abstraction of linguistic concepts and moreover to identify conceptual structures among data. The result is an ontological framework able to effectively analyze complex text structures and to reveal dependencies within the data. The polarity identification is carried out in terms of a support vector machine algorithm that performs binary feature classification, which can correspond to positive or negative polarity. [PERSON] and [PERSON] [85] have developed an opinion mining framework for the Portuguese language. The feature identification and selection are conducted by using the Movie Ontology [88], while the polarity identification takes place in terms of Portuguese opinion lexicons.