A MapReduce Clustering Approach for Sentiment Analysis Using Big Data

Khan, Mudassir; Alam, Mahtab; Basheer, Shakila; Ansari, Mohd Dilshad; Kumar, Neeraj

doi:10.1007/978-981-19-2350-0_22

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…In recent years, natural language processing-based sentiment analysis has become a popular research topic (Khan et al, 2022;Yadav & Vishwakarma, 2020a). Some researchers have begun to explore the application of sentiment analysis to the study of travel reviews in an effort to comprehend the emotional experiences that tourists have with various destinations, attractions, and services.…”

Section: Related Workmentioning

confidence: 99%

A TBGAV-Based Image-Text Multimodal Sentiment Analysis Method for Tourism Reviews

Zhang,

Wang,

2023

International Journal of Information Technology and Web Engineering

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To overcome limitations in existing methods for sentiment analysis of tourism reviews, the authors propose an image-text multimodal sentiment analysis method (TBGAV). It consists of three modules: image sentiment extraction, text sentiment extraction, and image-text fusion. The image sentiment extraction module employs a pre-trained VGG19 model to capture sentiment features. The text sentiment extraction module utilizes the tiny bidirectional encoder representations from transformers (TinyBERT) model, incorporating the bidirectional recurrent neural network and attention (BiGRU-Attention) module for deeper sentiment semantics. The image-text fusion module employs the dual linear fusion approach to correlate image-text links and the maximum decision-making approach for high-precision sentiment prediction. TBGAV achieves superior performance on the Yelp dataset with accuracy, recall rates, and F1 scores of 77.51%, 78.01%, and 78.34%, respectively, outperforming existing methods. Accordingly, TBGAV is expected to help improve travel-related recommender systems and marketing strategies.

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Section: Related Workmentioning

confidence: 99%

A TBGAV-Based Image-Text Multimodal Sentiment Analysis Method for Tourism Reviews

Zhang,

Wang,

2023

International Journal of Information Technology and Web Engineering

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Aspect-based sentiment analysis on multi-domain reviews through word embedding

Gopalachari

Gupta

Rakesh

et al. 2023

Journal of Intelligent Systems

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The finest resource for consumers to evaluate products is online product reviews, and finding such reviews that are accurate and helpful can be difficult. These reviews may sometimes be corrupted, biased, contradictory, or lacking in detail. This opens the door for customer-focused review analysis methods. A method called “Multi-Domain Keyword Extraction using Word Vectors” aims to streamline the customer experience by giving them reviews from several websites together with in-depth assessments of the evaluations. Using the specific model number of the product, inputs are continuously grabbed from different e-commerce websites. Aspects and key phrases in the reviews are properly identified using machine learning, and the average sentiment for each keyword is calculated using context-based sentiment analysis. To precisely discover the keywords in massive texts, word embedding data will be analyzed by machine learning techniques. A unique methodology developed to locate trustworthy reviews considers several criteria that determine what makes a review credible. The experiments on real-time data sets showed better results compared to the existing traditional models.

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A MapReduce Clustering Approach for Sentiment Analysis Using Big Data

Cited by 2 publications

References 7 publications

A TBGAV-Based Image-Text Multimodal Sentiment Analysis Method for Tourism Reviews

A TBGAV-Based Image-Text Multimodal Sentiment Analysis Method for Tourism Reviews

Aspect-based sentiment analysis on multi-domain reviews through word embedding

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