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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.