46,551 research outputs found
Automatically extracting polarity-bearing topics for cross-domain sentiment classification
Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning
Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
Sentiment analysis of user-generated reviews or comments on products and
services in social networks can help enterprises to analyze the feedback from
customers and take corresponding actions for improvement. To mitigate
large-scale annotations on the target domain, domain adaptation (DA) provides
an alternate solution by learning a transferable model from other labeled
source domains. Existing multi-source domain adaptation (MDA) methods either
fail to extract some discriminative features in the target domain that are
related to sentiment, neglect the correlations of different sources and the
distribution difference among different sub-domains even in the same source, or
cannot reflect the varying optimal weighting during different training stages.
In this paper, we propose a novel instance-level MDA framework, named
curriculum cycle-consistent generative adversarial network (C-CycleGAN), to
address the above issues. Specifically, C-CycleGAN consists of three
components: (1) pre-trained text encoder which encodes textual input from
different domains into a continuous representation space, (2) intermediate
domain generator with curriculum instance-level adaptation which bridges the
gap across source and target domains, and (3) task classifier trained on the
intermediate domain for final sentiment classification. C-CycleGAN transfers
source samples at instance-level to an intermediate domain that is closer to
the target domain with sentiment semantics preserved and without losing
discriminative features. Further, our dynamic instance-level weighting
mechanisms can assign the optimal weights to different source samples in each
training stage. We conduct extensive experiments on three benchmark datasets
and achieve substantial gains over state-of-the-art DA approaches. Our source
code is released at: https://github.com/WArushrush/Curriculum-CycleGAN.Comment: Accepted by WWW 202
How to Fine-Tune BERT for Text Classification?
Language model pre-training has proven to be useful in learning universal
language representations. As a state-of-the-art language model pre-training
model, BERT (Bidirectional Encoder Representations from Transformers) has
achieved amazing results in many language understanding tasks. In this paper,
we conduct exhaustive experiments to investigate different fine-tuning methods
of BERT on text classification task and provide a general solution for BERT
fine-tuning. Finally, the proposed solution obtains new state-of-the-art
results on eight widely-studied text classification datasets
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