346 research outputs found
Featuring, Detecting, and Visualizing Human Sentiment in Chinese Micro-Blog
2015-2016 > Academic research: refereed > Publication in refereed journa
From Independent Prediction to Re-ordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identification
Emotion cause identification aims at identifying the potential causes that
lead to a certain emotion expression in text. Several techniques including rule
based methods and traditional machine learning methods have been proposed to
address this problem based on manually designed rules and features. More
recently, some deep learning methods have also been applied to this task, with
the attempt to automatically capture the causal relationship of emotion and its
causes embodied in the text. In this work, we find that in addition to the
content of the text, there are another two kinds of information, namely
relative position and global labels, that are also very important for emotion
cause identification. To integrate such information, we propose a model based
on the neural network architecture to encode the three elements (, text
content, relative position and global label), in an unified and end-to-end
fashion. We introduce a relative position augmented embedding learning
algorithm, and transform the task from an independent prediction problem to a
reordered prediction problem, where the dynamic global label information is
incorporated. Experimental results on a benchmark emotion cause dataset show
that our model achieves new state-of-the-art performance and performs
significantly better than a number of competitive baselines. Further analysis
shows the effectiveness of the relative position augmented embedding learning
algorithm and the reordered prediction mechanism with dynamic global labels.Comment: Accepted by AAAI 201
SENTIMENT ANALYSIS OF CHINESE MICROBLOG MESSAGE USING NEURAL NETWORK-BASED VECTOR REPRESENTATION FOR MEASURING REGIONAL PREJUDICE
Regional prejudice is prevalent in Chinese cities in which native residents and migrants lack a basic level of trust in the other group. Like Twitter, Sina Weibo is a social media platform where people actively engage in discussions on various social issues. Thus, it provides a good data source for measuring individuals’ regional prejudice on a large scale. We find that a resentful tone dominates in Weibo messages related to migrants. In this paper, we propose a novel approach, named DKV, for recognizing polarity and direction of sentiment for Weibo messages using distributed real-valued vector representation of keywords learned from neural networks. Such a representation can project rich context information (or embedding) into the vector space, and subsequently be used to infer similarity measures among words, sentences, and even documents. We provide a comprehensive performance evaluation to demonstrate that by exploiting the keyword embeddings, DKV paired with support vector machines can effectively recognize a Weibo message into the predefined sentiment and its direction. Results demonstrate that our method can achieve the best performances compared to other approaches
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