Open AccessTEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM
Author(s) -
C. Arunabala,
P. Jwalitha,
Soniya Nuthalapati
Publication year - 2019
Publication title -
international journal of research - granthaalayah
Language(s) - English
Resource type - Journals
eISSN - 2394-3629
pISSN - 2350-0530
DOI - 10.29121/granthaalayah.v7.i6.2019.761
Subject(s) - sentiment analysis , computer science , support vector machine , artificial intelligence , convolutional neural network , generalization , semantics (computer science) , convolution (computer science) , contrast (vision) , natural language processing , pattern recognition (psychology) , machine learning , artificial neural network , mathematics , mathematical analysis , programming language
The traditional text sentiment analysis method is mainly based on machine learning. However, its dependence on emotion dictionary construction and artificial design and extraction features makes the generalization ability limited. In contrast, depth models have more powerful expressive power, and can learn complex mapping functions from data to affective semantics better. In this paper, a Convolution Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed. The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis based on CNNs and SVM