Integrated BERT embeddings, BiLSTM-BiGRU and 1-D CNN model for binary sentiment classification analysis of movie reviews

Gupta, Bhart; Prakasam, P.; Velmurugan, T.

doi:10.1007/s11042-022-13155-w

Cited by 13 publications

(11 citation statements)

References 43 publications

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“…CNN was proposed for text classification and achieved excellent results on trained word vectors [24]. In terms of RNN, variants of RNN-based models Long Short-Term Memory (LSTM) [26] and Gated Recurrent Unit (GRU) [27] are commonly used. LSTM text sentiment classification model was proposed based on the RNN text classification model [26].…”

Section: A Text Classification Based On Deep Learningmentioning

confidence: 99%

“…Based on the idea of feature fusion and fuses, the external features of words in the LSTM model enrich the text features and improve the performance of the model [25]. The BiLSTM model was proposed to accurately distinguish between personality traits and explicit emotions [27]. In recent 10 years, with the development of large-scale deep neural network technology, deep learning methods represented by convolutional neural network (CNN) [24] and recurrent neural network (RNN) [25] have received extensive attention.…”

Section: A Text Classification Based On Deep Learningmentioning

confidence: 99%

“…BERT-CNN is proposed for the classification of candidate causal sentences [24], Chinese sensitive text information [29] and fake news on COVID-19 [30,31], making significantly better effect than the most advanced baseline model. BERT-BiLSTM is used in medical text inference [16], sentiment analysis based on social media online comments [32] and movie reviews [27]. It can also complete the extraction of local features of Chinese medical texts, helping improve the medical service [26].…”

Section: A Text Classification Based On Deep Learningmentioning

confidence: 99%

“…When the RNN model is trained for back propagation, the gradient cannot be passed on for a long time in a longer sequence. So, the gradient will disappear, and the gated recurrent unit (GRU) [27] network can be used to solve the problem of long-term memory. However, the GRU network can only process one-way time series, and the contextual information in the text is highly correlated.…”

Section: A Developing and Validating The Deep Learning Modelmentioning

confidence: 99%

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Detecting Novelty Seeking From Online Travel Reviews: A Deep Learning Approach

et al. 2023

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Travel online reviews is important experience related information for understanding an inherent personality trait, novelty seeking (NS), which influences tourism motivation and the choice of tourism destinations. Manual classification of these reviews is challenging due to their high volume and unstructured nature. This paper aims to develop a classification framework and deep learning model to overcome these limitations. A multi-dimensional classification framework was created for NS personality trait that includes four dimensions synthesized from prior literature: relaxation seeking, experience seeking, arousal seeking and boredom alleviation. Based on 30 000 reviews from TripAdvisor we propose a deep learning model using Bidirectional Encoder Representations from Transformers (BERT)-Bidirectional Gated Recurrent Unit (BiGRU) to recognize NS automatically from the reviews. The classifier based on BERT-BiGRU and NS multi-dimensional scales achieved precision and F1 scores of 93.4% and 93.3% respectively, showing that NS personality trait can be relatively accurately recognized. This study also demonstrates that the classifier based on multi-dimensional NS scales can produce satisfactory results using the deep learning model. The findings also indicate that the BERT-BiGRU model achieves the best effect compared to the same kind of deep learning models. Moreover, it proves that personality traits can be automatically identified from travel reviews based on computational techniques. For practical purposes, this study provides a comprehensive classification framework for NS, which can be used in marketing and recommendation systems operating in the tourism industry.

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Section: A Text Classification Based On Deep Learningmentioning

confidence: 99%

Section: A Text Classification Based On Deep Learningmentioning

confidence: 99%

Section: A Text Classification Based On Deep Learningmentioning

confidence: 99%

Section: A Developing and Validating The Deep Learning Modelmentioning

confidence: 99%

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Detecting Novelty Seeking From Online Travel Reviews: A Deep Learning Approach

et al. 2023

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“…The existing deep learning models of emotion analysis mainly use word vector and are based on recurrent neural network. Deep learning-based architecture is superior to the machine learning methods through accuracy and low complexity [ 5 ]. However, neural network models only input word-level vectors into neural network and predict the emotion.…”

Section: Introductionmentioning

confidence: 99%

Integrating BERT Embeddings and BiLSTM for Emotion Analysis of Dialogue

Gou

Yan²

2023

Computational Intelligence and Neuroscience

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Dialogue system is an important application of natural language processing in human-computer interaction. Emotion analysis of dialogue aims to classify the emotion of each utterance in dialogue, which is crucially important to dialogue system. In dialogue system, emotion analysis is helpful to the semantic understanding and response generation and is great significance to the practical application of customer service quality inspection, intelligent customer service system, chatbots, and so on. However, it is challenging to solve the problems of short text, synonyms, neologisms, and reversed word order for emotion analysis in dialogue. In this paper, we analyze that the feature modeling of different dimensions of dialogue utterances is helpful to achieve more accurate sentiment analysis. Based on this, we propose the BERT (bidirectional encoder representation from transformers) model that is used to generate word-level and sentence-level vectors, and then, word-level vectors are combined with BiLSTM (bidirectional long short-term memory) that can better capture bidirectional semantic dependencies, and word-level and sentence-level vectors are connected and inputted to linear layer to determine emotions in dialogue. The experimental results on two real dialogue datasets show that the proposed method significantly outperforms the baselines.

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