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1 |
Deep reinforcement learning for robust emotional classification in facial expression recognition
یادگیری تقویتی عمیق برای طبقه بندی عاطفی قوی در تشخیص بیان چهره-2020 For emotion classification in facial expression recognition (FER), the performance of both traditional
statistical methods and state-of-the-art deep learning methods are highly dependent on the quality of
data. Traditional methods use image preprocessing (such as smoothing and segmentation) to improve
image quality. However, the results still fail to meet the quality requirements of the emotion classifiers
in FER. To address the above issues, this paper proposed a novel framework based on reinforcement
learning for pre-selecting useful images(RLPS) for emotion classification in FER, which is made up
of two modules: image selector and rough emotion classifier. Image selector is used to select useful
images for emotion classification through reinforcement strategy and rough emotion classifier acts as
a teacher to train image selector. Our framework improves classification performance by improving
the quality of the dataset and can be applied to any classifier. Experiment results on RAF-DB, ExpW,
and FER2013 datasets show that the proposed strategy achieves consistent improvements compared
with the state-of-the-art emotion classification methods in FER.1 Keywords: Emotion classification | Reinforcement learning | Image selector | Deep neural network |
مقاله انگلیسی |
2 |
Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion
طبقه بندی احساسات عمیق مبتنی بر یادگیری متن ارزشیابی مبتنی بر همجوشی چند ویژگی-2019 Sentiment analysis concerns the study of opinions expressed in a text. Due to the huge amount of
reviews, sentiment analysis plays a basic role to extract significant information and overall
sentiment orientation of reviews. In this paper, we present a deep-learning-based method to
classify a users opinion expressed in reviews (called RNSA).
To the best of our knowledge, a deep learning-based method in which a unified feature set
which is representative of word embedding, sentiment knowledge, sentiment shifter rules, statistical
and linguistic knowledge, has not been thoroughly studied for a sentiment analysis. The
RNSA employs the Recurrent Neural Network (RNN) which is composed by Long Short-Term
Memory (LSTM) to take advantage of sequential processing and overcome several flaws in traditional
methods, where order and information about the word are vanished. Furthermore, it uses
sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the following
drawbacks: words with similar semantic context but opposite sentiment polarity; contextual polarity;
sentence types; word coverage limit of an individual lexicon; word sense variations. To verify the
effectiveness of our work, we conduct sentence-level sentiment classification on large-scale review
datasets. We obtained encouraging result. Experimental results show that (1) feature vectors
in terms of (a) statistical, linguistic and sentiment knowledge, (b) sentiment shifter rules and (c)
word-embedding can improve the classification accuracy of sentence-level sentiment analysis; (2)
our method that learns from this unified feature set can obtain significant performance than one
that learns from a feature subset; (3) our neural model yields superior performance improvements
in comparison with other well-known approaches in the literature. Keywords: Deep learning | Sentiment analysis | Natural language processing | Neural network |
مقاله انگلیسی |