Sentiment analysis for cruises in Saudi Arabia on social media platforms using machine learning algorithms

Journal of Big Data

Table 1 Summary of previous studies

References	Data source	Method	Domain	Language	Size of data
[10]	Instagram, Twitter, Facebook, WhatsApp	Analysis and procedure	Emotion	Dutch	1201
[11]	Instagram, Twitter, Facebook	RF	Music	English	86 albums
[12]	Instagram	Dialogues, interviews	Tourists	English	–
[13]	Snapchat	Deep learning	Tourists	Arabic	–
[14]	Snapchat	Lexicon, ML	Economic	Arabic	1435 restaurant-goers
[3]	Snapchat	Content, Interviews	News	English	726 snaps
[15]	Snapchat	memory sampling	SA	English	online survey
[8]	Snapchat	NB, SVM	SA	English	textual data
[16]	Twitter	ML	SA	Arabic	151,500 tweets
[17]	Twitter	CNN, LSTM	SA	Arabic	–
[18]	Twitter	NB, Google prediction API	SA	English	120,000 tweets
[19]	Twitter	Word frequency	Tourists	English	42,785 tweets
[20]	Twitter	PCA	SA	English	2116 tweets
[21]	Twitter	lexicons	Tourists	English	53,546 tweets
[22]	TripAdvisor	SVM	Tourists	English	Reviews
[23]	TripAdvisor	ML	Tourists	English	2116 tweets
[24]	Online reviews	ML	Tourists	English	–
[25]	Questionnaire	DT	Tourists	English	1361 responses
[26]	Social media	NB, SVM	SA	English	2500 web pages
[27]	Social media	ML	Tourists	English	942 Regions