Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings
Unsupervised Cross-domain Sentiment Classification is the task of adapting a sentiment classifier trained on a particular domain (source domain), to a different domain (target domain), without requiring any labeled data for the target domain. By adapting an existing sentiment classifier to previously unseen target domains, we can avoid the cost for manual data annotation for the target domain. We model this problem as embedding learning, and construct three objective functions that capture: (a) distributional properties of pivots (i.e., common features that appear in both source and target domains), (b) label constraints in the source domain documents, and (c) geometric properties in the unlabeled documents in both source and target domains. Unlike prior proposals that first learn a lower-dimensional embedding independent of the source domain sentiment labels, and next a sentiment classifier in this embedding, our joint optimisation method learns embeddings that are sensitive to sentiment classification. Experimental results on a benchmark dataset show that by jointly optimising the three objectives we can obtain better performances in comparison to optimising each objective function separately, thereby demonstrating the importance of task-specific embedding learning for cross-domain sentiment classification. Among the individual objective functions, the best performance is obtained by (c). Moreover, the proposed method reports cross-domain sentiment classification accuracies that are statistically comparable to the current state-of-the-art embedding learning methods for cross-domain sentiment classification.
There is only one authority responsible for attribute management and key distribution. This only-one-authority scenario can bring a single-point bottleneck on both security and performance. Once the authority is compromised, an adversary can easily obtain the only-one-authority’s master key, then he/she can generate private keys of any attribute subsetto decrypt the specific encrypted data. Crash or offline of a specific authority will make that private keys of all attributes in attribute subset maintained by this authority cannot be generated and distributed, which will still influence the whole system’s effective operation.
This only-one-authority scenario can bring a single-point bottleneck on both security and performance.
These CP-ABE schemes are still far from being widely used for access control in public cloud storage.
Crash or offline of a specific authority will make that private keys of all attributes in attribute subset maintained by this authority cannot be generated and distributed, which will still influence the whole system’s effective operation.
the access structure is not flexible enough to satisfy complex environments. Subsequently,much effort has been made to deal with the disadvantages in the early schemes.
In this project, A Robust and Verifiable Threshold Multi-Authority
Access Control System in Public Cloud Storage have three modules .
- User module
- MultiauthorityAccess control
- Public cloud storage.
In this module, Users are having authentication and security to access the detail which is presented in the system. Before accessing or searching the details user should have the account in that otherwise they should register first.
Multi-authority Access control:
We conduct a threshold multi-authority CP-ABE access control scheme for
public cloud storage, named TMACS, in which multiple authorities jointly manage a uniform attribute set
To the best of our knowledge, we are the first to design a multiauthority access control architecture to deal with the problem.
To satisfy this hybrid scenario, we conduct a hybrid multi-authority access control scheme, by combining the traditional multi-authority scheme with our proposed TMACS.
Public Cloud Storage:
Cloud storage is an important service of cloud computing which provides services for data owners to outsource data to store in cloud via Internet.
The cloud server is always online and managed by the cloud provider. Usually, the cloud server and its provider is assumed “honest-but-curious”.
The cloud server does nothing but provide a platform for owners storing and sharing their encrypted data.The cloud server doesn’t conduct data access control for owners.
Hardware – Pentium
Speed – 1.1 GHz
RAM – 1GB
Hard Disk – 20 GB
Key Board – Standard Windows Keyboard
Mouse – Two or Three Button Mouse
Monitor – SVGA
Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings Cross-Domain Sentiment Classification Using Sentiment Sensitive EmbeddingsCross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings