The objective of the paper is to demonstrate the viability of analyzing online data. It displays a framework which after effects pattern investigation that will be shown as results with various segments introducing positive, negative and neutral. It is challenging task to summarize opinion about the products due to diversity and size. Mining online opinion mining is a difficult text classification task of sentiment analysis. Multivariate content technique called Feature Relation Network that considers semantic data, influencing the syntactic connections between n-gram features. FRN empowers the consideration of heterogeneous n-gram features for improved opinion classification, by joining syntactic data about n-gram relations. FRN selects the features in a more computationally effective way than numerous multivariate and hybrid methods. Appropriate feature selection and representation with sentiment analysis, accuracies using support vector mechanism sentiment analysis; the task of text pre-processing is to be explored.
Ashwini.M.Baikerikar : Department of Computer Science and Technology, Department of Technology, Shivaji University,
Kolhapur, Maharashtra, India.
P.C.Bhaskar : Department of Electronics and Communication Technology, Department of Technology, Shivaji University,
Kolhapur, Maharashtra, India.
Sentiment analysis; Text pre-processing; Feature Relation Network (FRN); Support Vector Machine (SVM)
In sentiment analysis feature selection, that emerges as a challenging area with lots of obstacles as it involves natural language processing. The challenge of this field is to develop the machines ability to understand text as human readers do. In this paper, we analyzed the part of text pre-processing in sentiment analysis, experimental results that demonstrate with appropriate feature selection and representation, sentiment analysis correctness using SVM in this area may be increased up to the level achieved in topic classification. Various pre-processing methods are used to reduce the noise in the text in addition to using chi-squared method to remove unwanted features that does not affect its orientation. The level of accuracy achieved on the two data sets is comparable to the sort of accuracy that can be achieved in topic categorizing. Concluding that hybrid method for feature selection can be the future direction in the field of feature selection in sentiment analysis.
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