6,313 research outputs found
Connotation Frames: A Data-Driven Investigation
Through a particular choice of a predicate (e.g., "x violated y"), a writer
can subtly connote a range of implied sentiments and presupposed facts about
the entities x and y: (1) writer's perspective: projecting x as an
"antagonist"and y as a "victim", (2) entities' perspective: y probably dislikes
x, (3) effect: something bad happened to y, (4) value: y is something valuable,
and (5) mental state: y is distressed by the event. We introduce connotation
frames as a representation formalism to organize these rich dimensions of
connotation using typed relations. First, we investigate the feasibility of
obtaining connotative labels through crowdsourcing experiments. We then present
models for predicting the connotation frames of verb predicates based on their
distributional word representations and the interplay between different types
of connotative relations. Empirical results confirm that connotation frames can
be induced from various data sources that reflect how people use language and
give rise to the connotative meanings. We conclude with analytical results that
show the potential use of connotation frames for analyzing subtle biases in
online news media.Comment: 11 pages, published in Proceedings of ACL 201
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture
The World Wide Web holds a wealth of information in the form of unstructured
texts such as customer reviews for products, events and more. By extracting and
analyzing the expressed opinions in customer reviews in a fine-grained way,
valuable opportunities and insights for customers and businesses can be gained.
We propose a neural network based system to address the task of Aspect-Based
Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic
Sentiment Analysis. Our proposed architecture divides the task in two subtasks:
aspect term extraction and aspect-specific sentiment extraction. This approach
is flexible in that it allows to address each subtask independently. As a first
step, a recurrent neural network is used to extract aspects from a text by
framing the problem as a sequence labeling task. In a second step, a recurrent
network processes each extracted aspect with respect to its context and
predicts a sentiment label. The system uses pretrained semantic word embedding
features which we experimentally enhance with semantic knowledge extracted from
WordNet. Further features extracted from SenticNet prove to be beneficial for
the extraction of sentiment labels. As the best performing system in its
category, our proposed system proves to be an effective approach for the
Aspect-Based Sentiment Analysis
A Unified Model for Opinion Target Extraction and Target Sentiment Prediction
Target-based sentiment analysis involves opinion target extraction and target
sentiment classification. However, most of the existing works usually studied
one of these two sub-tasks alone, which hinders their practical use. This paper
aims to solve the complete task of target-based sentiment analysis in an
end-to-end fashion, and presents a novel unified model which applies a unified
tagging scheme. Our framework involves two stacked recurrent neural networks:
The upper one predicts the unified tags to produce the final output results of
the primary target-based sentiment analysis; The lower one performs an
auxiliary target boundary prediction aiming at guiding the upper network to
improve the performance of the primary task. To explore the inter-task
dependency, we propose to explicitly model the constrained transitions from
target boundaries to target sentiment polarities. We also propose to maintain
the sentiment consistency within an opinion target via a gate mechanism which
models the relation between the features for the current word and the previous
word. We conduct extensive experiments on three benchmark datasets and our
framework achieves consistently superior results.Comment: AAAI 201
A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews
Despite the recent advances in opinion mining for written reviews, few works
have tackled the problem on other sources of reviews. In light of this issue,
we propose a multi-modal approach for mining fine-grained opinions from video
reviews that is able to determine the aspects of the item under review that are
being discussed and the sentiment orientation towards them. Our approach works
at the sentence level without the need for time annotations and uses features
derived from the audio, video and language transcriptions of its contents. We
evaluate our approach on two datasets and show that leveraging the video and
audio modalities consistently provides increased performance over text-only
baselines, providing evidence these extra modalities are key in better
understanding video reviews.Comment: Second Grand Challenge and Workshop on Multimodal Language ACL 202
Attentional Encoder Network for Targeted Sentiment Classification
Targeted sentiment classification aims at determining the sentimental
tendency towards specific targets. Most of the previous approaches model
context and target words with RNN and attention. However, RNNs are difficult to
parallelize and truncated backpropagation through time brings difficulty in
remembering long-term patterns. To address this issue, this paper proposes an
Attentional Encoder Network (AEN) which eschews recurrence and employs
attention based encoders for the modeling between context and target. We raise
the label unreliability issue and introduce label smoothing regularization. We
also apply pre-trained BERT to this task and obtain new state-of-the-art
results. Experiments and analysis demonstrate the effectiveness and lightweight
of our model.Comment: 7 page
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