Customer feedback is an essential criterion for upcoming customers to learn from their experience with a company’s products. Customer reviews and ratings also help companies improve performance and figure out new methodologies to provide better services. This research concentrates on customer reviews and ratings to investigate which product a customer evaluates and its association with its recommendations. This work predicts the user recommendations in two modules. The first module performs sentiment analysis of customer reviews using the long short-term memory (LSTM) model, which estimates the probability of the customer’s sentiment about the airline’s services. The second module experimented over only various service aspect ratings on different airline services provided by customers. These two modules ensemble together to determine the predictive recommendations of the airlines. The obtained results reinforce the essential theoretical contribution to the literature on service appraisal, online review, and recommendations. In addition, our proposed ensemble approach will be helpful to those practitioners who wish to use any proposal that will provide a quick and essential vision by bringing together customer-generated reviews and ratings, thereby helping them in strategy designing, service improvement, and post-purchases planning. Also, forthcoming travelers may benefit from this proposed approach by assimilating an aggregating view of service quality.

With the fastest growth of information and communication technology (ICT), the availability of web content on social media platforms is increasing day by day. Sentiment analysis from online reviews drawing researchers’ attention from various organizations such as academics, government, and private industries. Sentiment analysis has been a hot research topic in Machine Learning (ML) and Natural Language Processing (NLP). Currently, Deep Learning (DL) techniques are implemented in sentiment analysis to get excellent results. This study proposed a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for sentiment analysis. Our proposed model is being applied with dropout, max pooling, and batch normalization to get results. Experimental analysis carried out on Airlinequality and Twitter airline sentiment datasets. We employed the Keras word embedding approach, which converts texts into vectors of numeric values, where similar words have small vector distances between them. We calculated various parameters, such as accuracy, precision, recall, and F1-measure, to measure the model’s performance. These parameters for the proposed model are better than the classical ML models in sentiment analysis. Our results analysis demonstrates that the proposed model outperforms with 91.3% accuracy in sentiment analysis.

Inference has been central problem for understanding and reasoning in artificial intelligence. Especially, Natural Language Inference is an interesting problem that has attracted the attention of many researchers. Natural language inference intends to predict whether a hypothesis sentence can be inferred from the premise sentence. Most prior works rely on a simplistic association between the premise and hypothesis sentence pairs, which is not sufficient for learning complex relationships between them. The strategy also fails to exploit local context information fully. Long Short Term Memory (LSTM) or gated recurrent units networks (GRU) are not effective in modeling long-term dependencies, and their schemes are far more complex as compared to Convolutional Neural Networks (CNN). To address this problem of long-term dependency, and to involve context for modeling better representation of a sentence, in this article, a general Self-Attentive Convolution Neural Network (SACNN) is presented for natural language inference and sentence pair modeling tasks. The proposed model uses CNNs to integrate mutual interactions between sentences, and each sentence with their counterparts is taken into consideration for the formulation of their representation. Moreover, the self-attention mechanism helps fully exploit the context semantics and long-term dependencies within a sentence. Experimental results proved that SACNN was able to outperform strong baselines and achieved an accuracy of 89.7% on the stanford natural language inference (SNLI) dataset.