Code:-
JPJA2398
Abstract:-
Proposed System:-
>We propose an unlabeled cross-domain sentiment classification method using spectral embeddings where we project both the words and the documents into the same lower dimensional embedding.</li> <li>The embedding learnt by our method enforces three important requirements.</li> <li>First, a set of domain independent features (also known as pivots) areselected from the source and target domains which must be mapped as close as possible in the embedded space.</li> <li>Second,friend closeness and enemy dispersion of the source domain labeled documents must be preserved. In other words, positively labeled documents must be embedded closer to each other and far from the negatively labeled documents. Likewise,negatively labeled documents must be embedded closer to each other and far from the positively labeled documents.</li> <li>Third, within each domain, the local geometry among the documents must be preserved. For example,unlabeled neighbour documents in the source domainmust be embedded closer to each other in the embedded space whereas, unlabeled neighbour documents in the targetdomain must be embedded closer to each other in the embedded space. Here, neighbour documents refer to similar documents in terms of their text content.</li> <li>We model each of the above-mentioned requirements as an objective function,and jointly optimise all three objective functions.</li>
Advantages of Proposed System:-
The proposed method can be easily extended to more than two sentiment classes.</li> <li>Our experimental results on a benchmark data set formulti-domain sentiment classification demonstrate that by jointly optimising the three objectives in many cases we obtain better classification accuracies than if we had optimized each objective separately.</li> <li>Even in cases where joint optimisation does not improve over the separately trained objectives, the performance obtained by the joint optimization method is never below that obtained by the best individually trained methods.</li> <li>This result shows the importance of learning embeddings that are sensitive to the final task at hand, which is sentiment classification.</li> <li>Moreover, the proposed method significantly outperforms several base lines and previously proposed embedding learning methods when applied to cross-domain sentiment classification.</li>
Hardware Requirements:-
Software Requirements:-
- Operating system : Windows 10/11.</li> <li style="text-align: justify;">Coding Language: Java.</li> <li style="text-align: justify;">Frontend : JSP, HTML, CSS, JavaScript.</li> <li style="text-align: justify;">IDE Tool : Apache Netbeans IDE 16.</li> <li style="text-align: justify;">Database: MySQL.</li>
Cost:-
Rs 2000
A three layer based Intelligent Data Privacy Protection Scheme in Cloud Storage
Code:
JPJA2398
Abstract:
Proposed System:
>We propose an unlabeled cross-domain sentiment classification method using spectral embeddings where we project both the words and the documents into the same lower dimensional embedding.</li> <li>The embedding learnt by our method enforces three important requirements.</li> <li>First, a set of domain independent features (also known as pivots) areselected from the source and target domains which must be mapped as close as possible in the embedded space.</li> <li>Second,friend closeness and enemy dispersion of the source domain labeled documents must be preserved. In other words, positively labeled documents must be embedded closer to each other and far from the negatively labeled documents. Likewise,negatively labeled documents must be embedded closer to each other and far from the positively labeled documents.</li> <li>Third, within each domain, the local geometry among the documents must be preserved. For example,unlabeled neighbour documents in the source domainmust be embedded closer to each other in the embedded space whereas, unlabeled neighbour documents in the targetdomain must be embedded closer to each other in the embedded space. Here, neighbour documents refer to similar documents in terms of their text content.</li> <li>We model each of the above-mentioned requirements as an objective function,and jointly optimise all three objective functions.</li>
Advantages of Proposed System:
The proposed method can be easily extended to more than two sentiment classes.</li> <li>Our experimental results on a benchmark data set formulti-domain sentiment classification demonstrate that by jointly optimising the three objectives in many cases we obtain better classification accuracies than if we had optimized each objective separately.</li> <li>Even in cases where joint optimisation does not improve over the separately trained objectives, the performance obtained by the joint optimization method is never below that obtained by the best individually trained methods.</li> <li>This result shows the importance of learning embeddings that are sensitive to the final task at hand, which is sentiment classification.</li> <li>Moreover, the proposed method significantly outperforms several base lines and previously proposed embedding learning methods when applied to cross-domain sentiment classification.</li>
Hardware Requirements:
Software Requirements:
- Operating system : Windows 10/11.</li> <li style="text-align: justify;">Coding Language: Java.</li> <li style="text-align: justify;">Frontend : JSP, HTML, CSS, JavaScript.</li> <li style="text-align: justify;">IDE Tool : Apache Netbeans IDE 16.</li> <li style="text-align: justify;">Database: MySQL.</li>
Cost:
Rs 2000
Additional Information
Tools Used:
Java
Cost:
₹Rs 2000
