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A major concern when incorporating large sets of diverse n-gram features for sentiment classification is the presence of noisy, irrelevant, and redundant attributes. These concerns can often make it difficult to harness the augmented discriminatory potential of extended feature sets. We propose a rule-based multivariate text feature selection method called Feature Relation Network (FRN) that considers semantic information and also leverages the syntactic relationships between n-gram features. FRN is intended to efficiently enable the inclusion of extended sets of heterogeneous n-gram features for enhanced sentiment classification. Experiments were conducted on three online review test beds in comparison with methods used in prior sentiment classification research. FRN outperformed the comparison univariate, multivariate, and hybrid feature selection methods; it was able to select attributes resulting in significantly better classification accuracy irrespective of the feature subset sizes. Furthermore, by incorporating syntactic information about n-gram relations, FRN is able to select features in a more computationally efficient manner than many multivariate and hybrid techniques.
The Administrator can upload the mobile details as well as see the user comments and rating. The admin can manage the mobile details such as edit the mobile information and delete the mobile details
In this module, the search consists of companies name, mobile prices, mobile features and mobile screen types. The search result is providing in the format of dynamic links. If the user clicks the dynamic link and then view the corresponding mobile details.
The user is giving comments to the particular mobile. Based on the user comment it will move to the positive or negative opinion. While entering the comments, user must enter the following details such as, user name, email id and user comment. The admin can view user comments and details.
The percentage within-one accuracy was incorporated since multiclass opinion classification, involving three or more classes, can be challenging given the relationship and subtle differences between semantically adjacent classes. Based on the user comments, the admin provides the rating for the particular mobile model.
The existing feature selection methods do not adequately address attribute relevance and redundancy issues, which are critical for text sentiment analysis.
We propose the use of a rich set of n-gram features spanning many fixed and variable n-gram categories. We couple the extended feature set with a feature selection method capable of efficiently identifying an enhanced subset of n-grams for opinion classification. The proposed Feature Relation Network is a rule-based multivariate n-gram feature selection technique that efficiently removes redundant or less useful n-grams, allowing for more effective n-gram feature sets. Experimental results reveal that the extended feature set and proposed feature selection method can improve opinion classification performance over existing selection methods. ADVANTAGE: 1. The proposed feature selection method can improve opinion classification performance. 2. The proposed Feature Relation Network is a rule-based multivariate n-gram feature selection technique that allowing for more effective n-gram feature sets.
In this project, Feature Relation Network(FRN) algorithm is proposed that considers semantic information to enhanced sentiment classification for n-gram features. By using this, customer can efficiently purchase the product related to each other instead of searching the whole product. FRN is able to select features in a more computationally efficient manner than many multivariate and hybrid techniques.