2 research outputs found
Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning
Given a target name, which can be a product aspect or entity, identifying its
aspect words and opinion words in a given corpus is a fine-grained task in
target-based sentiment analysis (TSA). This task is challenging, especially
when we have no labeled data and we want to perform it for any given domain. To
address it, we propose a general two-stage approach. Stage one extracts/groups
the target-related words (call t-words) for a given target. This is relatively
easy as we can apply an existing semantics-based learning technique. Stage two
separates the aspect and opinion words from the grouped t-words, which is
challenging because we often do not have enough word-level aspect and opinion
labels. In this work, we formulate this problem in a PU learning setting and
incorporate the idea of lifelong learning to solve it. Experimental results
show the effectiveness of our approach
Detecting Gang-Involved Escalation on Social Media Using Context
Gang-involved youth in cities such as Chicago have increasingly turned to
social media to post about their experiences and intents online. In some
situations, when they experience the loss of a loved one, their online
expression of emotion may evolve into aggression towards rival gangs and
ultimately into real-world violence. In this paper, we present a novel system
for detecting Aggression and Loss in social media. Our system features the use
of domain-specific resources automatically derived from a large unlabeled
corpus, and contextual representations of the emotional and semantic content of
the user's recent tweets as well as their interactions with other users.
Incorporating context in our Convolutional Neural Network (CNN) leads to a
significant improvement.Comment: 12 page