Cross-domain sentiment classification via spectral feature alignment

Pan, Sinno Jialin; Ni, Xiaochuan; Sun, Ju; Yang, Qiang; Chen, Zheng

doi:10.1145/1772690.1772767

Cited by 621 publications

(353 citation statements)

References 32 publications

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“…In the text field, researchers have proposed various methods to tackle this problem based on non-deep neural networks [4,[28][29][30][31][32][33][34][35][36]. Blitzer et al presented structural correspondence learning (SCL) for transfer learning [4].…”

Section: Cross-domain Transfer Learningmentioning

confidence: 99%

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A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

et al. 2017

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Learners in a massive open online course often express feelings, exchange ideas and seek help by posting questions in discussion forums. Due to the very high learner-to-instructor ratios, it is unrealistic to expect instructors to adequately track the forums, find all of the issues that need resolution and understand their urgency and sentiment. In this paper, considering the biases among different courses, we propose a transfer learning framework based on a convolutional neural network and a long short-term memory model, called ConvL, to automatically identify whether a post expresses confusion, determine the urgency and classify the polarity of the sentiment. First, we learn the feature representation for each word by considering the local contextual feature via the convolution operation. Second, we learn the post representation from the features extracted through the convolution operation via the LSTM model, which considers the long-term temporal semantic relationships of features. Third, we investigate the possibility of transferring parameters from a model trained on one course to another course and the subsequent fine-tuning. Experiments on three real-world MOOC courses confirm the effectiveness of our framework. This work suggests that our model can potentially significantly increase the effectiveness of monitoring MOOC forums in real time.

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Section: Cross-domain Transfer Learningmentioning

confidence: 99%

“…Words that occur more frequently are considered as pivot features from the source, as well as target domain. Pan et al proposed the spectral feature alignment (SFA) algorithm for cross-domain sentiment classification [28]. In SFA, a set of domain-independent sentiment words is identified at first.…”

Section: Cross-domain Transfer Learningmentioning

confidence: 99%

A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

et al. 2017

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“…This acts as a bridge for cross domain classification. Pan et al, [14] proposed an algorithm SFA to bridge gap between domain and domain independent words. Twitter data contains diverse topics from different domains and different topics which are unpredictable and labelling of data for each topic is needed.…”

Section: Related Workmentioning

confidence: 99%

“…[5] Few works [6][7] [8] in past have borrowed a link that connects feature that is topic dependent and a pre determined or common feature. However, such kind of links may not be applied over topics in twitter which are unpredictable.…”

Section: Introductionmentioning

confidence: 99%

Secured Classification of Sentiments on Topic Adaptive Dynamic Tweets

Perur¹,

Balakrishnan²

2016

IJRET

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“…Thus, domain modification is needed. It is found that existing research has used labeled data class from one area, unlabeled data class from the target area and general opinion words as features for adaptation [1,3,20,23].…”

Section: Introductionmentioning

confidence: 99%

Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

et al. 2016

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With the rapid development of the World Wide Web, electronic word-of-mouth interaction has made consumers active participants. Nowadays, a large number of reviews posted by the consumers on the Web provide valuable information to other consumers. Such information is highly essential for decision making and hence popular among the internet users. This information is very valuable not only for prospective consumers to make decisions but also for businesses in predicting the success and sustainability. In this paper, a Gini Index based feature selection method with Support Vector Machine (SVM) classifier is proposed for sentiment classification for large movie review data set. The results show that our Gini Index method has better classification performance in terms of reduced error rate and accuracy.

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Cross-domain sentiment classification via spectral feature alignment

Cited by 621 publications

References 32 publications

A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

Secured Classification of Sentiments on Topic Adaptive Dynamic Tweets

Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

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