Segment-based Fine-grained Emotion Detection for Chinese Text

Author(s):  
Odbal ◽  
Zengfu Wang
2018 ◽  
Author(s):  
Vasilios Mavroudis ◽  
Michael Veale

Physical retailers, who once led the way in tracking with loyalty cards and ‘reverse appends’, now lag begin online competitors. Yet we might be seeing these tables turn, as many increasingly deploy technologies ranging from simple sensors to advanced emotion detection systems, even enabling them to tailor prices and shopping experiences on a per-customer basis. Here, we examine these in-store tracking technologies in the retail context, and evaluate them from both technical and regulatory standpoints. We first introduce the relevant technologies in context, before considering privacy impacts, the current remedies individuals might seek through technology and the law, and those remedies’ limitations. To illustrate challenging tensions in this space we consider the feasibility of technical and legal approaches to both a) the recent ‘Go’ store concept from Amazon which requires fine-grained, multi-modal track- ing to function as a shop; and b) current challenges in opting in or out of increasingly pervasive passive Wi-Fi tracking. The ‘Go’ store presents significant challenges with its legality in Europe significantly unclear and unilateral, technical measures to avoid biometric tracking likely ineffective. In the case of MAC addresses, we see a difficult-to-reconcile clash between privacy-as-confidentiality and privacy-as-control, and suggest a technical framework which might help balance the two. Sig- nificant challenges exist when seeking to balance personalisation with privacy, and researchers must work together, including across the boundaries of preferred privacy definitions, to come up with solutions that draw on both technology and the legal frameworks to provide effective and proportionate protection. Retailers, simultaneously, must ensure that their track- ing is not just legal, but worthy of the trust of concerned data subjects.


2020 ◽  
Vol 34 (10) ◽  
pp. 13732-13733
Author(s):  
Annika Marie Schoene

This paper states the challenges in fine-grained target-dependent Sentiment Analysis for social media data using recurrent neural networks. First, the problem statement is outlined and an overview of related work in the area is given. Then a summary of progress and results achieved to date and a research plan and future directions of this work are given.


2012 ◽  
Vol 5s1 ◽  
pp. BII.S8966 ◽  
Author(s):  
Kim Luyckx ◽  
Frederik Vaassen ◽  
Claudia Peersman ◽  
Walter Daelemans

We present a system to automatically identify emotion-carrying sentences in suicide notes and to detect the specific fine-grained emotion conveyed. With this system, we competed in Track 2 of the 2011 Medical NLP Challenge, 14 where the task was to distinguish between fifteen emotion labels, from guilt, sorrow, and hopelessness to hopefulness and happiness. Since a sentence can be annotated with multiple emotions, we designed a thresholding approach that enables assigning multiple labels to a single instance. We rely on the probability estimates returned by an SVM classifier and experimentally set thresholds on these probabilities. Emotion labels are assigned only if their probability exceeds a certain threshold and if the probability of the sentence being emotion-free is low enough. We show the advantages of this thresholding approach by comparing it to a naïve system that assigns only the most probable label to each test sentence, and to a system trained on emotion-carrying sentences only.


2018 ◽  
Author(s):  
Emily Öhman ◽  
Kaisla Kajava ◽  
Jörg Tiedemann ◽  
Timo Honkela

2016 ◽  
Author(s):  
Jasy Suet Yan Liew ◽  
Howard R. Turtle

Author(s):  
Shreshtha Mundra ◽  
Anirban Sen ◽  
Manjira Sinha ◽  
Sandya Mannarswamy ◽  
Sandipan Dandapat ◽  
...  

Author(s):  
Richard S. Chemock

One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.


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