Machine Learning-Based Sentiment Analysis for Analyzing the Travelers Reviews on Egyptian Hotels

Mostafa, Lamiaa

doi:10.1007/978-3-030-44289-7_38

Cited by 31 publications

(16 citation statements)

References 22 publications

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“…The satisfactory management of these mentions may lead to a reduction in the number of negative comments on these aspects [37,38,53]. Despite this, some studies point out that the management of this reputation in rural accommodation establishments is very infrequent, owing to which this accumulation of negative mentions in certain districts may considerably reduce the reception of the number of tourists [58].…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, they are not often used in the analysis of the online reputation of the sector, where the use of techniques which do not consider territory predominates [24,27,28,34,53,54]. For this reason, we propose a methodology concentrating on the use of geostatistics, which allow us to find out the spatial distribution of each variable analysed and also to determine territorial patterns in the positive and negative mentions.…”

Section: Methodsmentioning

confidence: 99%

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An Analysis of Online Reputation Indicators by Means of Geostatistical Techniques—The Case of Rural Accommodation in Extremadura, Spain

Delgado

Martín

Gallego

2020

IJGI

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The advances achieved in ICT in recent years have led to numerous changes in a multitude of sectors including tourism. The origin of a new communicative reality which allows interaction between users by means of various web portals should be emphasised. In this sense, online reputation arises, which corresponds to the recording of the opinions which the tourist expresses on the Internet of the various tourist destinations and accommodation. This reputation has a significant influence on the processes of planning and choice of the final destination, in such a way that the recording of a number of negative mentions can lead to a decrease in the number of tourists visiting a place. It is for this reason that this study aimed to determine the situation of the online reputation of rural accommodation establishments located in the Autonomous Region of Extremadura by means of an analysis of the opinions recorded by the rural tourist on various web portals of some of the services provided. To do so, we resorted to the application of spatial statistical techniques to determine which districts show an aggregation of high or low values of both positive and negative mentions. At the same time, the strengths and weaknesses of the online reputation of these accommodation establishments were detected.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

An Analysis of Online Reputation Indicators by Means of Geostatistical Techniques—The Case of Rural Accommodation in Extremadura, Spain

Delgado

Martín

Gallego

2020

IJGI

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“…Researchers defined the sentiment analysis process into the following stages: data acquisition, data preparation, review analysis, and sentiment classification [13][14][15][16]. Data acquisition is the process of identifying the source of the sentiment text, data preparation include removing irrelevant terms, review analysis techniques such as Term-Frequency-Inverse Document Frequency (TF-IDF), Bag of words (BOW) and Word2vec and finally the classification stage which depends on machine learning techniques like Naïve Bayes (NB) and Support Vector Machine (SVM).…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Machine learning uses traditional mining algorithms such as Naive Bayes, Support Vector Machines and Neural Networks (NN). Naïve Bayes [14][15][16][17], the following section will explore some of the researches that implement the word embedding.…”

Section: Sentiment Analysismentioning

confidence: 99%

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Egyptian Student Sentiment Analysis Using Word2vec During the Coronavirus (Covid-19) Pandemic

Mostafa

2020

Advances in Intelligent Systems and Computing

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Education field is affected by the COVID-19 pandemic which also affects how universities, schools, companies and communities function. One area that has been significantly affected is education at all levels, including both undergraduate and graduate. COVID-19 pandemic emphasis the psychological status of the students since they changed their learning environment. E-learning process focuses on electronic means of communication and online support communities, however social networking sites help students manage their emotional and social needs during pandemic period which allow them to express their opinions without controls. The paper will propose a Sentiment Analysis Model that will analyze the sentiments of students in the learning process with in their pandemic using Word2vec technique and Machine Learning techniques. The sentiment analysis model will start with the processing process on the student's sentiment and selects the features through word embedding then uses three Machine Learning classifies which are Naïve Bayes, SVM and Decision Tree. Results including precision, recall and accuracy of all these classifiers are described in this paper. The paper helps understand the Egyptian student's opinion on learning process during COVID-19 pandemic.

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A Survey of Sentiment Analysis Based on Machine Learning

Lin¹,

Luo²

2020

Natural Language Processing and Chinese Computing

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Machine Learning-Based Sentiment Analysis for Analyzing the Travelers Reviews on Egyptian Hotels

Cited by 31 publications

References 22 publications

An Analysis of Online Reputation Indicators by Means of Geostatistical Techniques—The Case of Rural Accommodation in Extremadura, Spain

An Analysis of Online Reputation Indicators by Means of Geostatistical Techniques—The Case of Rural Accommodation in Extremadura, Spain

Egyptian Student Sentiment Analysis Using Word2vec During the Coronavirus (Covid-19) Pandemic

A Survey of Sentiment Analysis Based on Machine Learning

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