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Survey on Aspect-Level Sentiment Analysis
TLDR
An in-depth overview of the current state-of-the-art of aspect-level sentiment analysis is given, showing the tremendous progress that has been made in finding both the target, which can be an entity as such, or some aspect of it, and the corresponding sentiment.
Engineering Semantic Web Information Systems in Hera
TLDR
This paper addresses the Hera design methodology and shows how RDF(S) has proven its value in combining all relevant aspects of WIS design, thus illustrating how Hera allows the engineering of Semantic Web Information Systems (SWIS).
News personalization using the CF-IDF semantic recommender
TLDR
It is demonstrated that adapting TF-IDF with the semantics of a domain ontology, resulting in Concept Frequency - Inverse Document Frequency (CF-IDs), yields better results than using the original TF- IDF method.
Semantics-based news recommendation
TLDR
Two new methods based on concepts and their semantic similarities are proposed, from which the similarities between news items are derived, and test results show that SF-IDF and SS outperform the TF- IDF method on the F1-measure.
XAL: An Algebra For XML Query Optimization
TLDR
The resulting algebra has optimization laws similar to the known laws for transforming relational queries and is suitable for composability, optimizability, and semantics definition of a query language for XML data.
Exploiting emoticons in sentiment analysis
TLDR
How emoticons typically convey sentiment is analyzed and how to exploit this by using a novel, manually created emoticon sentiment lexicon in order to improve a state-of-the-art lexicon-based sentiment classification method.
Ontology-based news recommendation
TLDR
Athena, which is an extension to the existing Hermes framework, employs a user profile to store terms or concepts found in news items browsed by the user, and uses a traditional method based on TF-IDF, and several ontology-based methods to recommend new articles to the user.
Polarity analysis of texts using discourse structure
TLDR
Pathos, a framework which performs document sentiment analysis (partly) based on a document's discourse structure, is proposed, which hypothesizes that by splitting a text into important and less important text spans, and by subsequently making use of this information by weighting the sentiment conveyed by distinct text spans in accordance with their importance, it can improve the performance of a sentiment classifier.
Determining negation scope and strength in sentiment analysis
TLDR
This work compares several approaches to accounting for negation in sentiment analysis, differing in their methods of determining the scope of influence of a negation keyword.
COMMIT-P1WP3: A Co-occurrence Based Approach to Aspect-Level Sentiment Analysis
TLDR
A simple aspect detection algorithm, a co-occurrence based method for category detection and a dictionary based sentiment classification algorithm are presented, which focus mainly on the category detection method as it is most distinctive for the work.
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