A System for Semantically Enhanced, Multifaceted, Collaborative Access: Requirements and Architecture

2007 ◽  
Author(s):  
Jawed Siddiqi ◽  
Babak Akhghar ◽  
Nazaraf Shah ◽  
Fazilatur Rahman ◽  
Nahum Korda ◽  
...  
Keyword(s):  
Semantically Enhanced

Semantically enhanced Information Retrieval: An ontology-based approach

2011 ◽  
Vol 9 (4) ◽  
pp. 434-452 ◽  
Author(s):  
Miriam Fernández ◽  
Iván Cantador ◽  
Vanesa López ◽  
David Vallet ◽  
Pablo Castells ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Semantically Enhanced

scholarly journals Semantically-enhanced information extraction

2011 ◽  
Author(s):  
Hisham Assal ◽  
John Seng ◽  
Franz Kurfess ◽  
Emily Schwarz ◽  
Kym Pohl
Keyword(s):  
Information Extraction ◽  
Semantically Enhanced

Impact of Deep Learning on Semantic Sentiment Analysis

2021 ◽  
pp. 97-117
Author(s):  
Neha Gupta ◽  
Rashmi Agrawal
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Semantic Technologies ◽  
Sources Of Information ◽  
Accurate Analysis ◽  
Online Social Media ◽  
Semantic Orientation ◽  
Semantically Enhanced

Online social media (forums, blogs, and social networks) are increasing explosively, and utilization of these new sources of information has become important. Semantics plays a significant role in accurate analysis of an emotion speech context. Adding to this area, the already advanced semantic technologies have proven to increase the precision of the tests. Deep learning has emerged as a prominent machine learning technique that learns multiple layers or data characteristics and delivers state-of-the-art output. Throughout recent years, deep learning has been widely used in the study of sentiments, along with the growth of deep learning in many other fields of use. This chapter will offer a description of deep learning and its application in the analysis of sentiments. This chapter will focus on the semantic orientation-based approaches for sentiment analysis. In this work, a semantically enhanced methodology for the annotation of sentiment polarity in Twitter/ Facebook data will be presented.

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