Essay On Polarity Analysis

Abstract- Polarity classification of words is important for applications such as Opinion Mining and Sentiment Analysis. A number of sentiment word/sense dictionaries have numerous inaccuracies. The concept of polarity consistency of words/senses in sentiment dictionaries is used to reduce inaccuracies of sentiment words even in reviews of products. The polarity consistency problem is reduced by satisfiability problem and utilize two fast SAT solvers to detect inconsistencies in a sentiment dictionary. Feature Extraction is the basic step for finding the polarity of the given opinion and this uses sentiment dictionaries (like OF,AL,SWD,QW,etc.,.) to check the polarity of the sentiment words and opinions that are extracted using feature extraction.

Index Terms—Polarity classification, opinion mining, Feature Extraction, Sentiment dictionary.

I. INTRODUCTION

Sentiment analysis is a type of

It has applications in text classification, text filtering, analysis of product review, analysis of responses to surveys, and mining online discussions. We propose a method for identifying the polarity of words. We apply a Markov random walk model to a large word relatedness graph, producing a polarity estimate for any given word. A key advantage of the model is its ability to accurately and quickly assign a polarity sign and magnitude to any word. The method could be used both in a semi-supervised setting where a training set of labeled words is used, and in an unsupervised setting where a handful of seeds is used to define the two polarity classes. The method is experimentally tested using a manually labeled set of positive and negative words. It outperforms the state of the art methods in the semi-supervised setting. The results in the unsupervised setting is comparable to the best reported values. However, the proposed method is faster and does not need a large