Topic-based sentiment analysis of hotel reviews

Gharzouli, Mohamed; Hamama, Aimen Khalil; Khattabi, Zakaria

doi:10.1080/13683500.2021.1940107

Cited by 10 publications

(12 citation statements)

References 16 publications

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“…The most impressive performance was about 63.0% F1macro for spam detection. In [19], the authors proposed subject-focused resort opinion mining. They suggested a system that can either be used to examine a specific hotel or to differentiate between many hotels.…”

Section: Motivationmentioning

confidence: 99%

An Ensemble-Based Hotel Reviews System Using Naive Bayes Classifier

Awotunde¹,

Misra²,

Katta³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The task of classifying opinions conveyed in any form of text online is referred to as sentiment analysis. The emergence of social media usage and its spread has given room for sentiment analysis in our daily lives. Social media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various fields. Various subject matter can be encountered on social media platforms, such as movie product reviews, consumer opinions, and testimonies, among others, which can be used for sentiment analysis. The rapid uncovering of these web contents contains divergence of many benefits like profit-making, which is one of the most vital of them all. According to a recent study, 81% of consumers conduct online research prior to making a purchase. But the reviews available online are too huge and numerous for human brains to process and analyze. Hence, machine learning classifiers are one of the prominent tools used to classify sentiment in order to get valuable information for use in companies like hotels, game companies, and so on. Understanding the sentiments of people towards different commodities helps to improve the services for contextual promotions, referral systems, and market research. Therefore, this study proposes a sentiment-based framework detection to enable the rapid uncovering of opinionated contents of hotel reviews. A Naive Bayes classifier was used to process and analyze the dataset for the detection of the polarity of the words. The dataset from Datafiniti's Business Database obtained from Kaggle was used for the experiments in this study. The performance evaluation of the model shows a test accuracy of 96.08%, an F1-score of 96.00%, a precision of 96.00%, and a recall of 96.00%. The results were compared with state-of-the-art classifiers and showed a promising performance and much better in terms of performance metrics.

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Section: Motivationmentioning

confidence: 99%

An Ensemble-Based Hotel Reviews System Using Naive Bayes Classifier

Awotunde¹,

Misra²,

Katta³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Therefore, sentiment analysis remains an important tool to process and classify the opinions of customers and the quality of the provided services (Gharzouli et al, 2022). More and more sentiment analysis will need to be advanced in the tourism and travel field to capture the attitudes and experiences of visitors in tourism destinations.…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

Targeting Creativity Through Sentiment Analysis: A Survey on Bucharest City Tourism

Surugiu

Grădinaru

2023

Sage Open

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This study investigates the sentiments of Bucharest city visitors resulting from their interaction with various attractions perceived as being “creative.” The online reviews displayed on TripAdvisor were collected as a database for our research, covering the July 2016 to July 2022 period. The data were processed using R software and NVivo software intended for text analysis and the investigation of the sentiments’ distribution. The findings suggest that creative offer spurs positive and very positive sentiments among travelers. Tourists experienced joy and trust during their tourism products and services consumption. The creative products are encountered especially in restaurants and escape rooms and are less correlated with heritage, arts, museums, or architecture. These findings are very useful for local entrepreneurs, local authorities, and policymakers in order to find solutions to include more creative products and services in the city tourism offer. Even if the present research is limited to Bucharest city, a future investigation could address the creativity dimensions of other destinations in Romania and abroad, highlighting the specific tourist attractions that boost various sentiment types among tourists.

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“…These reservations and tickets are all obtained from tourism-related websites or apps (such as Ctrip and elong). For example, the process of booking a hotel may be influenced by the hotel's 'smart information' [65]. The location of the hotel [66], whether the hotel can be booked [67], the price at the time of booking, reviews of the authenticity of the hotel, and whether the reservation can be cancelled after the reservation are all part of tourists' perceptions of the smart tourism experience before they travel.…”

Section: Perception Of Smart Tourism Application (Psta)mentioning

confidence: 99%

Research on the Influencing Mechanism via Which Security Perception of Personal Information Affects Tourist Happiness: A Moderated Mediation Model

Zhang

Chen

2022

Sustainability

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Due to the continuous advancement of smart tourism and the improvement of tourists’ security perception of personal information (SPPI), SPPI has become an important factor affecting tourist happiness (TH). However, few studies have been conducted on the influencing mechanism via which SPPI affects TH. According to the risk perception theory and subjective wellbeing theory, the following conclusions were drawn from mixed methods research: (1) SPPI has a significant direct effect on TH; (2) SPPI has a significant indirect effect on TH through the perception of smart tourism application (PSTA); (3) the perception of smart tourism technology (PSTT) does not have a significant moderating effect between SPPI and PSTA or between PSTA and TH, but has a significant moderating effect between SPPI and TH. The discussion of this mechanism can not only promote the combined development of the two theories but also remind destinations or scenic spots to focus on improving tourists’ SPPI when developing smart tourism.

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Topic-based sentiment analysis of hotel reviews

Cited by 10 publications

References 16 publications

An Ensemble-Based Hotel Reviews System Using Naive Bayes Classifier

An Ensemble-Based Hotel Reviews System Using Naive Bayes Classifier

Targeting Creativity Through Sentiment Analysis: A Survey on Bucharest City Tourism

Research on the Influencing Mechanism via Which Security Perception of Personal Information Affects Tourist Happiness: A Moderated Mediation Model

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