on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80 % up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7 % over

develop a new cryptosystem for fine-grained sharing of encrypted data that we call Key-Policy Attribute-Based Encryption (KP-ABE). In our cryptosystem, ciphertexts are labeled with sets of attributes and private keys are associated with access structures that control which ciphertexts a user is able

Abstract. In this paper we investigate the use of latent variable structured prediction models for fine-grained sentiment analysis in the common situation where only coarse-grained supervision is available. Specifically, we show how sentencelevel sentiment labels can be effectively learned from

latent variable structured prediction models

it is uniquely constrained by the contextual range and sensitivity of each relationship in the network. The subsequent differential sharing of sentiments leads to the formation of subgroups with more internal stability than the group as a whole, and results in fission. The Ford-Fulkerson labeling algorithm

We address the rating-inference problem, wherein rather than simply decide whether a review is “thumbs up ” or “thumbs down”, as in previous sentiment analysis work, one must determine an author’s evaluation with respect to a multi-point scale (e.g., one to five “stars”). This task represents

for fine-grained data sharing. It supports static program analysis of information flow, so that programs can be certified to permit only acceptable information flows, while largely avoiding the overhead of run-time checking. The article introduces the language Jif, an extension to Java that provides static

to intelligently understand text and extract a wide range of information, it is useful to more precisely determine the semantic classes of entities mentioned in unstructured text. This paper defines a fine-grained set of 112 tags, formulates the tagging problem as multi-class, multi-label classification, describes

. Sentiment analysis systems working on the (sub-)sentence level, however, are difficult to develop since shorter textual segments rarely carry enough information to determine their polarity out of context. In this paper, therefore, we present a fully automatic framework for fine-grained sentiment analysis

and also the strength of the opinions is an enormous help for people and organizations who are willing to use this information for their goals. In this paper, we present a rule-based approach for German sentiment analysis. The proposed model provides a fine-grained annotation for German texts, which