Sentiment Polarity Classification Using Conjure of Genetic Algorithm and Differential Evolution Methods for Optimized Feature Selection

Title:Sentiment Polarity Classification Using Conjure of Genetic Algorithm and Differential Evolution Methods for Optimized Feature Selection

Volume: 13 Issue: 6

Author(s): Jeevanandam Jotheeswaran*S. Koteeswaran

Affiliation:

• Center for Online Education, GLA University, UP,India

Keywords: Sentiment Analysis (SA), Genetic Algorithm (GA), Multi-Layer Perceptron (MLP), Differential Evolution (DE).

Abstract:

Objectives: Sentiment Analysis (SA) has a big role in Big data applications regarding consumer attitude detection, brand/product positioning, customer relationship management and market research. SA is a natural language processing method to track the public mood on a specific product. SA builds a system to collect/examine opinions on a product in comments, blog posts, re- views or tweets. Machine learning applicable to Sentiment Analysis belongs to supervised classifi- cation in general.

Methods: Two sets of documents, training and test set are required in machine learning based classification: Training set is used by classifiers to learn documents differentiating character- istics; it is thus called supervised learning.

Results: Test sets validate the classifier’s performance. Se- mantic orientation approach to SA is unsupervised learning because it requires no prior training for mining data. It measures how far a word is either positive or negative. This paper uses a hybrid GA- DE optimization technique for sentiment classification to classify features from movie reviews and medical data.

Conclusion: Our research has enhanced the variables on learning rate as well as momentum values which are optimized by genetic approach that in turn improve the accuracy of classification procedure.

Cite this article as:

Jotheeswaran Jeevanandam *, Koteeswaran S. , Sentiment Polarity Classification Using Conjure of Genetic Algorithm and Differential Evolution Methods for Optimized Feature Selection, Recent Advances in Computer Science and Communications 2020; 13 (6) . https://dx.doi.org/10.2174/2213275911666180904110105