Entropy based classifier for cross-domain opinion mining

Deshmukh, Jyoti S; Tripathy, Amiya Kumar

doi:10.1016/j.aci.2017.03.001

Cited by 41 publications

(14 citation statements)

References 22 publications

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“…SFA method is adopted by Lin et al and proposed two methods cosine function and taxonomy‐based regression model (TBRM) for selecting the more identical model based on the target node 39 . Recently, to align the domain‐specific and domain‐independent features for CDSC, the author utilized the technique of SFA method and introduced modified maximum entropy along with bipartite graph clustering 40 . The limitations of these approaches are: (i) heavily dependent on the labeled dataset and (ii) manually selection of pivot features.…”

Section: Related Workmentioning

confidence: 99%

Cross‐domain sentiment classification using decoding‐enhanced bidirectional encoder representations from transformers with disentangled attention

Singh

Sachan

Patel

2022

Concurrency and Computation

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Cross-domain sentiment classification is a significant task of sentiment analysis that objectives to predict the opinion orientation of text documents in the target domain by using the source domain's learned classifier. Most of the existing approaches of domain-adaptation in sentiment classification focus on sharing low-dimensional features across the domain using domain independent and specific features to mitigate the gap between domains. Earlier cross-domain sentiment classification approaches mainly focused on document level and sentence level, they cannot consider the full impact of aspect words, position of the words, and long-term dependencies. To address this concern, we propose a model for cross-domain sentiment classification, which is based on decoding-enhanced BERT with disentangled attention (DeBERTa). DeBERTa is a pretrained language model based on transformer architecture. In this article, we perform sentence and aspect embedding to mine wordpiece information from text document. DeBERTa language-model utilize disentangled attention mechanism and an enhanced mask decoder to understand the expression features. Disentangled attention mechanism is used to encode each word into two vectors (i.e., content and position vector). In order to predict the masked tokens during model pretraining, an enhanced mask decoder is employed, which incorporates absolute positions in the decoding layer.Finally, experiments are conducted on the benchmark dataset that demonstrates the superiority of fine-tuned DeBERTa model for cross-domain sentiment classification tasks.

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Section: Related Workmentioning

confidence: 99%

Cross‐domain sentiment classification using decoding‐enhanced bidirectional encoder representations from transformers with disentangled attention

Singh

Sachan

Patel

2022

Concurrency and Computation

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“…Hu & Liu (2004) and Potdar et al (2016) have followed Lexicon-based approach to produce the summarization of product reviews collected from the amazon website. Deshmukh & Tripathy (2018) have proposed a semi-supervised approach for sentiment analysis using modified maximum entropy method. The work concentrated on collecting sentiment words from one domain which can be used to predict the sentiment words of another domain.…”

Section: Sentiment Analysismentioning

confidence: 99%

Aspect-based classification of product reviews using Hadoop framework

Rodrigues¹,

Chiplunkar²,

Fernandes³

2020

Cogent Engineering

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The advancement of e-commerce along with the quick development of product review discussion in the most recent decade, an enormous measure of sentiment data or reviews are produced which made it practically difficult for a customer to take an educated buy choice. A good number of customers share their conclusions about the product during these days. These reviews have an important role in customers' purchase-decision process. For a popular product there may be hundreds or thousands of reviews. This is difficult for a potential customer to go to each of them to make an educated choice on whether to buy the product or not. Also, this is difficult for manufacturer of the product to follow-up and to manage client opinions. In this scenario, the aspects-based sentimental analysis helps in analyzing the reviews and categorizing them into appropriate aspects. Aspect or feature refers to attributes or qualities of a product. The proposed work begins with collecting reviews from online shopping websites, identifying aspects and classifying opinion orientation of aspects with different sentiment analysis techniques using Hadoop framework. This paper proposes a new pattern-based method for aspect extraction and sentiment analysis which gives an accuracy in the ABOUT THE AUTHORS

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“…Xia et al [31] use the ensemble features such as part-of-speech tagging and word relation to establish an ensemble model for the NB, SVC and EM methods, and the experimental results are better than the traditional single machine learning models. Deshmuke et al [32] combine the improved maximum entropy model with the binary graph clustering model and achieve relatively high accuracy for the classification of affective words. Tang et al [33] analyze the applications of deep learning methods in sentiment analysis earlier and find that it is superior to traditional methods in sentiment classification, viewpoint extraction and emotion dictionary construction.…”

Section: Cross-domain Text Sentiment Analysismentioning

confidence: 99%

Cross-Domain Text Sentiment Analysis Based on CNN_FT Method

Meng

Long

et al. 2019

Information

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Transfer learning is one of the popular methods for solving the problem that the models built on the source domain cannot be directly applied to the target domain in the cross-domain sentiment classification. This paper proposes a transfer learning method based on the multi-layer convolutional neural network (CNN). Interestingly, we construct a convolutional neural network model to extract features from the source domain and share the weights in the convolutional layer and the pooling layer between the source and target domain samples. Next, we fine-tune the weights in the last layer, named the fully connected layer, and transfer the models from the source domain to the target domain. Comparing with the classical transfer learning methods, the method proposed in this paper does not need to retrain the network for the target domain. The experimental evaluation of the cross-domain data set shows that the proposed method achieves a relatively good performance.

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Entropy based classifier for cross-domain opinion mining

Cited by 41 publications

References 22 publications

Cross‐domain sentiment classification using decoding‐enhanced bidirectional encoder representations from transformers with disentangled attention

Cross‐domain sentiment classification using decoding‐enhanced bidirectional encoder representations from transformers with disentangled attention

Aspect-based classification of product reviews using Hadoop framework

Cross-Domain Text Sentiment Analysis Based on CNN_FT Method

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