An Approach to Sentiment Analysis using Artificial Neural Network with Comparative Analysis of Different Techniques
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 2
Abstract
Abstract : Sentiment Analysis is the process of identifying whether the opinion or reviews expressed in a piece of work is positive, negative or neutral. Sentiment analysis is useful in social media monitoring to automatically characterize the overall feeling or mood of consumers as replicated in social media toward a specific brand or company and determine whether they are viewed positively or negatively on the web Sentiment Analysis has been widely used in classification of review of products and movie review ratings. This paper reviews the machine learning-based approaches to sentimentanalysis and brings out the salient features of techniques in place. The prominently used techniques and methods in machine learning-based sentiment analysis include - Naïve Bayes, Maximum Entropy and Support Vector Machine, K-nearest neighbour classification. Naïve Bayes has very simple representation but doesn't allow for rich hypotheses. Also theassumption of independence of attributes is too constraining. Maximum Entropy estimates the probability distribution from data, but it performs well with only dependent features. For SVM may provide the right kernel, but lacks the standardized way for dealing with multi-class problems. For improving the performance regarding correlation and dependencies between variables, an approach combining neural networks and fuzzy logic is often used
Authors and Affiliations
Pranali Borele , Dilipkumar A. Borikar
Keywords
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