Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model

Table 1

Recent related work for sentiment analysis on the US airline dataset.

Dataset	Authors	ML algorithm
US airlines	Umer et al. [12]	LR, VC
		SGD, SVC
		RF, LSTM
		CNN-LSTM
US airlines,; Airline quality	Jain et al. [11]	NB, LR
		DT, SVC
		LSTM, CNN
		CNN-LSTM
US airlines	Monika et al. [21]	RNN, LSTM
US airlines	Rustam et al. [22]	SVC, LR
		RF, DT, CC
		SGD, GNB
		GBC, ETC
		AdaBoost, VC
		GBM, LSTM
US airlines	Kumar and Zymbler [23]	SVC, ANN, CNN
US airlines	Hakh et al. [24]	AdaBoost
		DT, SVC
		NB, RF
		K-NN
US airlines	Acosta et al. [25]	SVC, LR

CC: calibrated classifier [26–28], VC: voting classifier, SVC: support vector classifier, DT: decision tree, ANNs: artificial neural networks [29], RNN: recurrent neural network, LSTM: long short-term memory, GNB: Gaussian naive Bayes, K-NN: K-nearest neighbor [30–32], ETC: extra trees classifier, NB: naïve Bayes, GBM: gradient boosting machine, RF: random forest, LR: logistic regression, and SGD: stochastic gradient descent.