HMRFLR: A Hybrid Model for Sentiment Analysis of Social Media Surveillance on Airlines

Singh, Neelam; Upreti, Manisha

doi:10.1007/s11277-023-10592-0

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…The authors developed a machine learning model to detect Conversation Polarity Change (CPC) to detect the ultimate sentiment polarity that customers will harbor as their conversations evolve with the brand. Modeling customer engagement on social media has also been reported in various industries, including transportation [23], finance [24], healthcare [25], sports [26], etc.…”

Section: Customer Advocacy In Online Environmentsmentioning

confidence: 99%

Knowledge Graph Construction for Social Customer Advocacy in Online Customer Engagement

Abu-Salih,

Alotaibi

2023

Technologies

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The rise of online social networks has revolutionized the way businesses and consumers interact, creating new opportunities for customer word-of-mouth (WoM) and brand advocacy. Understanding and managing customer advocacy in the online realm has become crucial for businesses aiming to cultivate a positive brand image and engage with their target audience effectively. In this study, we propose a framework that leverages the pre-trained XLNet- (bi-directional long-short term memory) BiLSTM- conditional random field (CRF) architecture to construct a Knowledge Graph (KG) for social customer advocacy in online customer engagement (CE). The XLNet-BiLSTM-CRF model combines the strengths of XLNet, a powerful language representation model, with BiLSTM-CRF, a sequence labeling model commonly used in natural language processing tasks. This architecture effectively captures contextual information and sequential dependencies in CE data. The XLNet-BiLSTM-CRF model is evaluated against several baseline architectures, including variations of BERT integrated with other models, to compare their performance in identifying brand advocates and capturing CE dynamics. Additionally, an ablation study is conducted to analyze the contributions of different components in the model. The evaluation metrics, including accuracy, precision, recall, and F1 score, demonstrate that the XLNet-BiLSTM-CRF model outperforms the baseline architectures, indicating its superior ability to accurately identify brand advocates and label customer advocacy entities. The findings highlight the significance of leveraging pre-trained contextual embeddings, sequential modeling, and sequence labeling techniques in constructing effective models for constructing a KG for customer advocacy in online engagement. The proposed framework contributes to the understanding and management of customer advocacy by facilitating meaningful customer-brand interactions and fostering brand loyalty.

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Section: Customer Advocacy In Online Environmentsmentioning

confidence: 99%

Knowledge Graph Construction for Social Customer Advocacy in Online Customer Engagement

Abu-Salih,

Alotaibi

2023

Technologies

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Sentiment analysis model for Airline customers’ feedback using deep learning techniques

Samir,

Abd-Elmegid,

Marie

2023

International Journal of Engineering Business Management

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Sentiment analysis (SA) has recently developed an automated approach for assessing sentiment, emotion, and these reviews or opinions to extract relevant and subjective information from text-based data. Analyzing sentiment on social networks, such as Twitter, has become a powerful means of learning about the users’ opinions and better understanding and satisfaction. However, it consumes time and energy to disperse and collect surveys from clients, often inaccurate and inconsistent, and evaluating and improving the accuracy of the methods in sentiment analysis is being hindered by the challenges encountered in Natural Language Processing (NLP). This paper uses NLP, text analysis, biometrics, and computational linguistics to detect and extract replies, moods, or emotions from Skytrax Airline Customers' Feedback SACF data. This research uses deep learning models to analyze various approaches applied to small SACF to solve sentiment analysis problems. We applied word embedding (Glove embedding models) to improve the sentiment classification performance of a series of datasets extensively utilized for feature extractions. Finally, a comparative study has been conducted on the SACF data analysis utilizing deep learning (DL)for evaluating the performance of the different models and input features, which is Recurrent Neural Networks (RNN), long short-term memory (LSTM), Gated Recurrent Unit (GRU), 1D Convolutional Neural Networks (CONV1D), and Bidirectional Encoder Representations from Transformers (BERT) for application to big datasets in 2019. This approach was assessed using each classification technique; the precision, recall, f1-score, and accuracy metrics for sentiment analysis have been identified. And The results show that LSTM outperforms in classification accuracy; the outcome was 91%.

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HMRFLR: A Hybrid Model for Sentiment Analysis of Social Media Surveillance on Airlines

Cited by 2 publications

References 19 publications

Knowledge Graph Construction for Social Customer Advocacy in Online Customer Engagement

Knowledge Graph Construction for Social Customer Advocacy in Online Customer Engagement

Sentiment analysis model for Airline customers’ feedback using deep learning techniques

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