Textual Feedback Analysis: Review

Agrawal, Pratik; Alvi, A. S.

doi:10.1109/iccubea.2015.95

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…It may be challenging to keep track of guest comments and improvement recommendations, especially for major accommodation chains with a high volume of daily guests. [3]…”

Section: Introductionmentioning

confidence: 99%

A Review on Deriving Maintenance of Accommodations Via Text-based Feedback Analysis

Wickramasinghe¹,

Fernando²

2023

Preprint

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In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96\% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

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“…It may be challenging to keep track of guest comments and improvement recommendations, especially for major accommodation chains with a high volume of daily guests. [3]…”

Section: Introductionmentioning

confidence: 99%

A Review on Deriving Maintenance of Accommodations Via Text-based Feedback Analysis

Wickramasinghe¹,

Fernando²

2023

Preprint

View full text Add to dashboard Cite

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“…The details of these big data traits are provided in the section that follows. Five elements that define big data are as follows: Volume, Velocity (Speed), Variety, Value and Veracity (Realism) [2] [3]. The volume of big data is made up of the enormous amount of data that is produced daily by numerous businesses and consumers around the world.…”

mentioning

confidence: 99%

Evaluation of Responsive With Big Data Technologies

Ashif Ali, Dr. Nisarg Gandhewar

2023

tjjpt

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The volume of produced digital data & everyday usage is increasing as a result of rising man-machine interaction, process automation, and falling hardware and software costs. The vast amount of digital information produced per second worldwide in unstructured, structured, and semi-structured formats is referred to as big data in this context. Big data analytics is a new topic that has inspired researchers all over the world to create, develop, and implement a variety of mechanisms, technologies, architectures, and areas for evaluating the enormous amount of data created every day. Traditional database management systems struggle to make sense of the massive amounts of data that make up "big data." The paper describes a few analyses, including word count, sentiment analysis, and responsive analysis. Responsiveness is crucial for improving the system, identifying vulnerabilities, and ensuring that tasks are distributed fairly. Responsive is important data that can be used for making wise decisions. Responsive is significant for both strengths and weaknesses, not just when it identifies flaws. If the responsive analysis is performed incorrectly, the analysis's conclusion will likewise be incorrect. As a result, the system as a whole will be inaccurate because the pattern that was discovered will also be incorrect. We will utilize the Map-Reduce framework to create this suggested system for responsive analysis, and Hadoop will be used for storage.

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A Multi-filter BiLSTM-CNN Architecture for Vietnamese Sentiment Analysis

Thin

Nguyen

et al. 2020

Advances in Computational Collective Intelligence

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Textual Feedback Analysis: Review

Cited by 8 publications

References 14 publications

A Review on Deriving Maintenance of Accommodations Via Text-based Feedback Analysis

A Review on Deriving Maintenance of Accommodations Via Text-based Feedback Analysis

Evaluation of Responsive With Big Data Technologies

A Multi-filter BiLSTM-CNN Architecture for Vietnamese Sentiment Analysis

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