Twitter Text Mining for Sentiment Analysis on People’s Feedback about Oman Tourism

Ramanathan, Vallikannu; Meyyappan, T.

doi:10.1109/icbdsc.2019.8645596

Cited by 54 publications

(33 citation statements)

References 4 publications

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“…Many studies have conducted sentiment analysis in the field of tourism. One such study is the one by (Ramanathan & Meyyappan, 2019) which uses the Oman tourism ontology based on ConceptNet. The authors identify the entities (generally nouns) from each tweet using a part-of-speech tagger, and these are compared with concepts in the ontology.…”

Section: Sentiment and Emotion Detectionmentioning

confidence: 99%

Analyzing tourist data on Twitter: a case study in the province of Granada at Spain

Viñán-Ludeña

Campos

2021

JHTI

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PurposeThe main aim of this paper is to build an approach to analyze the tourist content posted on social media. The approach incorporates information extraction, cleaning, data processing, descriptive and content analysis and can be used on different social media platforms such as Instagram, Facebook, etc. This work proposes an approach to social media analytics in traveler-generated content (TGC), and the authors use Twitter to apply this study and examine data about the city and the province of Granada.Design/methodology/approachIn order to identify what people are talking and posting on social media about places, events, restaurants, hotels, etc. the authors propose the following approach for data collection, cleaning and data analysis. The authors first identify the main keywords for the place of study. A descriptive analysis is subsequently performed, and this includes post metrics with geo-tagged analysis and user metrics, retweets and likes, comments, videos, photos and followers. The text is then cleaned. Finally, content analysis is conducted, and this includes word frequency calculation, sentiment and emotion detection and word clouds. Topic modeling was also performed with latent Dirichlet association (LDA).FindingsThe authors used the framework to collect 262,859 tweets about Granada. The most important hashtags are #Alhambra and #SierraNevada, and the most prolific user is @AlhambraCultura. The approach uses a seasonal context, and the posted tweets are divided into two periods (spring–summer and autumn–winter). Word frequency was calculated and again Granada, Alhambra are the most frequent words in both periods in English and Spanish. The topic models show the subjects that are mentioned in both languages, and although there are certain small differences in terms of language and season, the Alhambra, Sierra Nevada and gastronomy stand out as the most important topics.Research limitations/implicationsExtremely difficult to identify sarcasm, posts may be ambiguous, users may use both Spanish and English words in their tweets and tweets may contain spelling mistakes, colloquialisms or even abbreviations. Multilingualism represents also an important limitation since it is not clear how tweets written in different languages should be processed. The size of the data set is also an important factor since the greater the amount of data, the better the results. One of the largest limitations is the small number of geo-tagged tweets as geo-tagging would provide information about the place where the tweet was posted and opinions of it.Originality/valueThis study proposes an interesting way to analyze social media data, bridging tourism and social media literature in the data analysis context and contributes to discover patterns and features of the tourism destination through social media. The approach used provides the prospective traveler with an overview of the most popular places and the major posters for a particular tourist destination. From a business perspective, it informs managers of the most influential users, and the information obtained can be extremely useful for managing their tourism products in that region.

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Section: Sentiment and Emotion Detectionmentioning

confidence: 99%

Analyzing tourist data on Twitter: a case study in the province of Granada at Spain

Viñán-Ludeña

Campos

2021

JHTI

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“…authors conclude that adding Twitter details does not improve accuracy significantly. In [36], the authors applied sentiment analysis on around 4,432 tweets to collect opinions on Oman tourism, they build a domain-specific ontology for Oman tourism using Concept Net. Researchers constructed a sentiment lexicon based on three existing lexicons, SentiStrength, SentWordNet, and Opinion lexicon.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis in English Texts

Alshamsi¹,

Bayari²,

Salloum³

2020

Adv. sci. technol. eng. syst. j.

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“…In the work of [72], sentiment analysis is applied to find people's reviews about tourism in Oman on Twitter data. A domain-specific ontology is created, and entities identified by a part-of-speech (POS) tagger are compared with the domain-specific concept.…”

Section: Analysis Of Online Reviewsmentioning

confidence: 99%

A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis

Shafqat

Byun

2019

Sustainability

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With rapid advancements in internet applications, the growth rate of recommendation systems for tourists has skyrocketed. This has generated an enormous amount of travel-based data in the form of reviews, blogs, and ratings. However, most recommendation systems only recommend the top-rated places. Along with the top-ranked places, we aim to discover places that are often ignored by tourists owing to lack of promotion or effective advertising, referred to as under-emphasized locations. In this study, we use all relevant data, such as travel blogs, ratings, and reviews, in order to obtain optimal recommendations. We also aim to discover the latent factors that need to be addressed, such as food, cleanliness, and opening hours, and recommend a tourist place based on user history data. In this study, we propose a cross mapping table approach based on the location’s popularity, ratings, latent topics, and sentiments. An objective function for recommendation optimization is formulated based on these mappings. The baseline algorithms are latent Dirichlet allocation (LDA) and support vector machine (SVM). Our results show that the combined features of LDA, SVM, ratings, and cross mappings are conducive to enhanced performance. The main motivation of this study was to help tourist industries to direct more attention towards designing effective promotional activities for under-emphasized locations.

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Twitter Text Mining for Sentiment Analysis on People’s Feedback about Oman Tourism

Cited by 54 publications

References 4 publications

Analyzing tourist data on Twitter: a case study in the province of Granada at Spain

Analyzing tourist data on Twitter: a case study in the province of Granada at Spain

Sentiment Analysis in English Texts

A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis

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