Hotel review analysis for the prediction of business using deep learning approach

Hossen, Md. Sagar; Jony, Anik Hassan; Tabassum, Tasfia; Islam, Md. Tanvir; Rahman, Md. Mahfujur; Khatun, Tania

doi:10.1109/icais50930.2021.9395757

Cited by 16 publications

(9 citation statements)

References 19 publications

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“…The machine learning methods include Naïve Bayes [7], Logistic Regression [13], Decision Tree [25], K-nearest neighbor (KNN) [13], and AdaBoost [26]. The deep learning methods include Gated Recurrent Unit (GRU) [27], Long Short-Term Memory (LSTM) [27], Bidirectional Long Short-Term Memory (BiLSTM) [28], Convolutional Neural Network-LSTM (CNN-LSTM) [29], and Convolutional Neural Network-BiLSTM (CNN-BiLSTM) [30]. methods on all three datasets.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network

et al. 2022

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Due to the rapid development of technology, social media has become more and more common in human daily life. Social media is a platform for people to express their feelings, feedback, and opinions. To understand the sentiment context of the text, sentiment analysis plays the role to determine whether the sentiment of the text is positive, negative, neutral or any other personal feeling. Sentiment analysis is prominent from the perspective of business or politics where it highly impacts the strategic decision making. The challenges of sentiment analysis are attributable to the lexical diversity, imbalanced dataset and long-distance dependencies of the texts. In view of this, a data augmentation technique with GloVe word embedding is leveraged to synthesize more lexically diverse samples by similar word vector replacements. The data augmentation also focuses on the oversampling of the minority classes to mitigate the imbalanced dataset problems. Apart from that, the existing sentiment analysis mostly leverages sequence models to encode the long-distance dependencies. Nevertheless, the sequence models require a longer execution time as the processing is done sequentially. On the other hand, the Transformer models require less computation time with parallelized processing. To that end, this paper proposes a hybrid deep learning method that combines the strengths of sequence model and Transformer model while suppressing the limitations of sequence model. Specifically, the proposed model integrates Robustly optimized BERT approach and Long Short-Term Memory for sentiment analysis. The Robustly optimized BERT approach maps the words into a compact meaningful word embedding space while the Long Short-Term Memory model captures the longdistance contextual semantics effectively. The experimental results demonstrate that the proposed hybrid model outshines the state-of-the-art methods by achieving F1-scores of 93%, 91%, and 90% on IMDb dataset, Twitter US Airline Sentiment dataset, and Sentiment140 dataset, respectively.

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Section: Experimental Results and Analysismentioning

confidence: 99%

RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network

et al. 2022

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“…Experiment used accuracy, sensitivity, RMSE, and MAE. The suggested approach (KMC+SVM) is contrasted with existing techniques like "Convolutional Neural Network" (CNN) [22], "deep belief network" (DBN) [23], and "long short term memory" (LSTM) [24].…”

Section: Resultsmentioning

confidence: 99%

A Novel K-Means Clustered Support Vector Machine Technique for Prediction of Consumer Decision-Making Behaviour

Zhou

Salleh

Wang

et al. 2022

IJRITCC

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A greater number of consumers are using social networks to express their feedback about the level of service provided by hotels. Online reviews from patrons can be used as a forum to enhance the level of service of hotels. Customer reviews are indeed a reliable and dependable source that aid diners in determining the quality of their cuisine. It is critical to develop techniques for evaluating client feedback on hotel services. In order to accurately anticipate the consumers' decision-making behaviors based on hotel internet evaluations, this study proposes a novel K-Means Clustered Support Vector Machine (KMC+SVM) technique. Principal Component Analysis (PCA) is employed to determine the characteristics from the preprocessed data while the Min-Max normalization approach is used to standardize the raw data. The performance of the suggested technique is then evaluated and contrasted with a few other methods that are currently in use in terms of accuracy, sensitivity, RMSE, and MAE. The findings demonstrated that segmenting customers based on their online evaluations can accurately predict their choices and assist hotel management in establishing priorities for service quality enhancements.

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“…We have used two other datasets named Twitter US Airline 9 (This data originally came from Crowd ower's 10 Data for Everyone library.) and Sentiment140 (Go et al, 2009).…”

Section: Datasetmentioning

confidence: 99%

“…The model outperformed state-ofthe-art methods with accuracy of 94.9%, 91.77%, and 89.81% on IMDb, Twitter US Airline Sentiment dataset, and Sentiment140 dataset. The paper(Hossen et al, 2021) introduces a deep learning methodology employing LSTM and GRUs to train hotel review data for the purpose of identifying…”

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confidence: 99%

Social network textual data classification through a hybrid word embedding approach and Bayesian conditional-based multiple classifiers

Ghorbanali

2024

Preprint

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Sentiment analysis (SA) of text holds a pivotal role in today's digital age, particularly within the realm of social media networks. The analysis of textual sentiments emerges as a critical facet of NLP. In social media, individuals extensively engage with a multitude of texts and opinions. SA empowers us to delve into and discover these opinions, sentiments, and viewpoints, thereby extracting valuable insights on a wide array of subjects. The significance of word embeddings for processing textual data lies in their ability to represent words as dense vectors, enabling machines to capture semantic relationships and contextual nuances, thereby enhancing various natural language processing tasks. There are two popular and famous models, BERT and GloVe, for embedding words. Currently, GloVe is considered one of the most precise approaches. However, this method does not take into account the sentiment information present in texts. Consequently, we opted to utilize pre-trained BERT models, which have been trained on extensive text corpora, in combination with the GloVe model to address this limitation. This study leverages a hybrid word embedding model combining BERT and GloVe. Several classifiers are employed to analyze text sentiment. At the decision level, we employ Bayesian Conditional to integrate current results with prior decisions. When combining previous decisions with new ones, the model achieves higher accuracy by refining or adjusting decisions in light of new evidence. Our approach demonstrates notable results, showcasing its practical significance. The results of the experiments on IMDB, Sentiment140, and Twitter US Airline datasets demonstrate that the proposed approach has achieved favorable results, with accuracies of 0.958, 0.925, and 0.946 respectively. These results are considered acceptable when compared to those of other similar studies.

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Hotel review analysis for the prediction of business using deep learning approach

Cited by 16 publications

References 19 publications

RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network

RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network

A Novel K-Means Clustered Support Vector Machine Technique for Prediction of Consumer Decision-Making Behaviour

Social network textual data classification through a hybrid word embedding approach and Bayesian conditional-based multiple classifiers

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