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Dual Sentiment Analysis: Considering Two Sides of One Review
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Dual Sentiment Analysis: Considering Two Sides of One Review

Category : Data Mining


Sub Category : JAVA


Project Code : ITJDM02


Project Abstract

Dual Sentiment Analysis:

Considering Two Sides of One Review

 

ABSTRACT

Bag-of-words (BOW) is now the most popular way to model text in statistical machine learning approaches in sentiment analysis. However, the performance of BOW sometimes remains limited due to some fundamental deficiencies in handling the polarity shift problem. We propose a model called dual sentiment analysis (DSA), to address this problem for sentiment classification. We first propose a novel data expansion technique by creating a sentiment-reversed review for each training and test review. On this basis, we propose a dual training algorithm to make use of original and reversed training reviews in pairs for learning a sentiment classifier, and a dual prediction algorithm to classify the test reviews by considering two sides of one review.

           

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

In existing, the sentimental analysis will not

give the correct reviews for the given queries.

A large number of researches aimed to enhance BOW by incorporating linguistic knowledge.

Although the BOW model is very simple and quite efficient in topic-based text classification, it is actually not very suitable for sentiment classification because it disrupts the word order, breaks the syntactic structures, and discards some semantic information.

 

 

PROPOSED CONCEPT:-

In proposed, we propose a simple yet efficient model, called dual sentiment analysis (DSA), to address the polarity shift problem in sentiment classification.

We first propose a data expansion technique by creating sentiment reversed reviews. The original and reversed reviews are constructed in a one-to-one correspondence.

 EXISTING ALGORITHM:-

BOW model.

PROPOSED ALGORITHM:-

Dual sentiment analysis (DSA) model.

ALGORITHM DEFINITION:-

BOW model is very simple and quite efficient in topic-based text classification; it is actually not very suitable for sentiment classification.

It disrupts the word order, breaks the syntactic structures, and discards some semantic information.

ALGORITHM DEFINITION:-

We propose a dual training (DT) algorithm and a dual prediction (DP) algorithm respectively, to make use of the original and reversed samples in pairs for training a statistical classifier and make predictions.

In DT, the classifier is learnt by maximizing a combination of likelihoods of the original and reversed training data set.

In DP, predictions are made by considering two sides of one review.

DRAWBACKS:-

Giving lot of negative reviews.

No positive reviews possible.

ADVANTAGES:-

Giving only the positive reviews.

Here we can change the negative reviews.

 
 
 
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