A Commodity Review Sentiment Analysis Based on BERT-CNN Model

Dong, Jing; He, Feijuan; Ye, Guo; Zhang, Huibing

doi:10.1109/icccs49078.2020.9118434

Cited by 37 publications

(17 citation statements)

References 6 publications

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“…It could be because the CNN algorithm might extract local and global features very well from the vectors using the convolutional, the pooling, and the fully connected (dense) layers, which can maintain semantic context meaning on text data. This finding supports studies on sentiment analysis of a commodity review and stance detection for credibility analysis of information on social media conducted by [24], [25]. These studies showed that BERT embeddings and CNN obtained better results than single CNN that ignores relation contextual semantics on text.…”

Section: Discussionsupporting

confidence: 86%

On the Role of Text Preprocessing in BERT Embedding-based DNNs for Classifying Informal Texts

Kurniasih¹,

Manik²

2022

IJACSA

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Due to highly unstructured and noisy data, analyzing society reports in written texts is very challenging. Classifying informal text data is still considered a difficult task in natural language processing since the texts could contain abbreviated words, repeating characters, typos, slang, et cetera. Therefore, text preprocessing is commonly performed to remove the noises and make the texts more structured. However, we argued that most tasks of preprocessing are no longer required if suitable word embeddings approach and deep neural network (DNN) architecture are correctly chosen. This study investigated the effects of text preprocessing in fine-tuning a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using various DNN architectures such as multilayer perceptron (MLP), long short-term memory (LSTM), bidirectional long-short term memory (Bi-LSTM), convolutional neural network (CNN), and gated recurrent unit (GRU). Various experiments were conducted using numerous learning rates and batch sizes. As a result, text preprocessing had insignificant effects on most models such as LSTM, Bi-LSTM, and CNN. Moreover, the combination of BERT embeddings and CNN produced the best classification performance.

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Section: Discussionsupporting

confidence: 86%

On the Role of Text Preprocessing in BERT Embedding-based DNNs for Classifying Informal Texts

Kurniasih¹,

Manik²

2022

IJACSA

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“…BERT is designed to be able to distinguish a process that has a different meaning. BERT uses unlabeled in designing deep bidirectional representations by moving the context left and right across all layers [11], [19]. The results of the previously trained BERT model can then be tuned with one output layer to create a model for performing various tasks including answering questions, language inference without substantial modification of the task-specific architecture.…”

Section: Propose Methodsmentioning

confidence: 99%

Tuned bidirectional encoder representations from transformers for fake news detection

Pardamean

Pardede

2021

IJEECS

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Online medias are currently the dominant source of Information due to not being limited by time and place, fast and wide distributions. However, inaccurate news, or often referred as fake news is a major problem in news dissemination for online medias. Inaccurate news is information that is not true, that is engineered to cover the real information and has no factual basis. Usually, inaccurate news is made in the form of news that has mass appeal and is presented in the guise of genuine and legitimate news nuances to deceive or change the reader's mind or opinion. Identification of inaccurate news from real news can be done with natural language processing (NLP) technologies. In this paper, we proposed bidirectional encoder representations from transformers (BERT) for inaccurate news identification. BERT is a language model based on deep learning technologies and it has found effective for many NLP tasks. In this study, we use transfer learning and fine-tuning to adapt BERT for inaccurate news identification. The experiments show that our method could achieve accuracy of 99.23%, recall 99.46%, precision 98.86%, and F-Score of 99.15%. It is largely better than traditional method for the same tasks.

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“…Literature also revealed studies exploring the more advanced embedding technique, BERT and its variants in improving sentiment analysis for reviews. For instance, [34] improved sentiment analysis for commodity reviews using BERT-CNN with F-score results indicating the combination of BERT-CNN (84.3%) to be the best compared to BERT (82%) and CNN (70.9%). Similarly, [12] developed SenBERT-CNN to analyze JD.com (mobile phone merchant) reviews by combining BERT and CNN, the latter of which was used to extract deep features of the text.…”

Section: Deep Learning Approaches To Reviewsmentioning

confidence: 99%

“…BERT-variant models were pre-trained by incorporating the context of the word within the text in Wikipedia and BooksCorpus [44], and the embedding are then used through a classifier for predictions. As they produce contextualized word embeddings, they produce state-of-the-art results on Natural Language Processing tasks [12,34]. The BERT-base model is a bi-directional (both left-to-right and rightto-left direction) transformer for pre-training over a lot of unlabeled textual data to learn a language representation that can be used to fine-tune for specific classification tasks (see [44] for further details).…”

Section: Feature Extractionmentioning

confidence: 99%

A deep learning approach in predicting products’ sentiment ratings: a comparative analysis

Balakrishnan

Shi

Law³

et al. 2021

J Supercomput

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A Commodity Review Sentiment Analysis Based on BERT-CNN Model

Cited by 37 publications

References 6 publications

On the Role of Text Preprocessing in BERT Embedding-based DNNs for Classifying Informal Texts

On the Role of Text Preprocessing in BERT Embedding-based DNNs for Classifying Informal Texts

Tuned bidirectional encoder representations from transformers for fake news detection

A deep learning approach in predicting products’ sentiment ratings: a comparative analysis

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