A novel feature extraction methodology for sentiment analysis of product reviews

Chen, Xin; Xue, Yun; Zhao, Hongya; Lü, Xin; Hu, Xiaohui; Ma, Zhihao

doi:10.1007/s00521-018-3477-2

Cited by 25 publications

(8 citation statements)

References 25 publications

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“…When comparing the BiGRU RNN network with other neural networks such as Convolutional Neural Network (CNN), Long Short-term Memory (LSTM), and CNN+LSTM, our BiGRULA achieved better accuracy with 0.894 over the test dataset than the other three network models with accuracies of 0.881, 0.812, and 0.858, respectively. Table 2 also shows the comparison accuracy values with other known machine learning approaches as available in literature [6,24,25] using the IMDB dataset. FPCD feature vectors combined with the generalized TF_IDF vectors + Naïve Bayes (G_TF-IDF + FPCD + NB), Word2vec + K-Nearest Neighbor (Word2vec + KNN), and frequent, pseudo-consecutive phrase feature with high discriminative ability + Support Vector Machine (FPCD + SVM) achieved the highest accuracy among their feature extraction methods, while, compared to our model, their accuracy values still could not compare.…”

Section: Results and Analysismentioning

confidence: 99%

Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors

et al. 2018

Sustainability

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Sentiment analysis of online tourist reviews is playing an increasingly important role in tourism. Accurately capturing the attitudes of tourists regarding different aspects of the scenic sites or the overall polarity of their online reviews is key to tourism analysis and application. However, the performances of current document sentiment analysis methods are not satisfactory as they either neglect the topics of the document or do not consider that not all words contribute equally to the meaning of the text. In this work, we propose a bidirectional gated recurrent unit neural network model (BiGRULA) for sentiment analysis by combining a topic model (lda2vec) and an attention mechanism. Lda2vec is used to discover all the main topics of review corpus, which are then used to enrich the word vector representation of words with context. The attention mechanism is used to learn to attribute different weights of the words to the overall meaning of the text. Experiments over 20 NewsGroup and IMDB datasets demonstrate the effectiveness of our model. Furthermore, we applied our model to hotel review data analysis, which allows us to get more coherent topics from these reviews and achieve good performance in sentiment classification.

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Section: Results and Analysismentioning

confidence: 99%

Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors

et al. 2018

Sustainability

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“…The n-gram feature extraction used in this study does not consider the semantic similarity or the discriminative ability of words. Therefore, enhanced n-gram representations [29] are recommended to reduce the dimensionality and sparsity of the data. The application of an effective feature selection method may also lead to lower computational complexity and improved time efficiency [30].…”

Section: Discussionmentioning

confidence: 99%

Opinion Mining of Consumer Reviews Using Deep Neural Networks with Word-Sentiment Associations

Hájek

Barushka

Munk

2020

IFIP Advances in Information and Communication Technology

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Automated opinion mining of consumer reviews is becoming increasingly important due to the rising influence of reviews on online retail shopping. Existing approaches to automated opinion classification rely either on sentiment lexicons or supervised machine learning. Deep neural networks perform this classification task particularly well by utilizing dense document representation in terms of word embeddings. However, this representation model does not consider the sentiment polarity or sentiment intensity of the words. To overcome this problem, we propose a novel model of deep neural network with word-sentiment associations. This model produces richer document representation that incorporates both word context and word sentiment. Specifically, our model utilizes pre-trained word embeddings and lexicon-based sentiment indicators to provide inputs to a deep feed-forward neural network. To verify the effectiveness of the proposed model, a benchmark dataset of Amazon reviews is used. Our results strongly support integrated document representation, which shows that the proposed model outperforms other existing machine learning approaches to opinion mining of consumer reviews.

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“…As with similar approaches which extract features from review data [90], [91], non-domain-dependent phrases are removed. These phrases are removed as they do not relate to the needs of the product type being searched for.…”

Section: ) Removal Of Non-discriminate Phrasesmentioning

confidence: 99%

Using Machine Learning to Improve Lead Times in the Identification of Emerging Customer Needs

2022

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A novel feature extraction methodology for sentiment analysis of product reviews

Cited by 25 publications

References 25 publications

Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors

Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors

Opinion Mining of Consumer Reviews Using Deep Neural Networks with Word-Sentiment Associations

Using Machine Learning to Improve Lead Times in the Identification of Emerging Customer Needs

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