An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews

Ray, Bidhan Chandra; Garain, Avishek; Sarkar, Ram

doi:10.1016/j.asoc.2020.106935

Cited by 128 publications

(66 citation statements)

References 21 publications

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“…The four different variants of SVM were used for sentiment classification using the features obtained by the Genetic Algorithm after optimization. Ray, Garain & Sarkar (2021) proposed a hotel recommendation system using sentiment analysis of hotel reviews. First, an ensemble of the BERT model was used to find the word embeddings.…”

Section: Background and Related Workmentioning

confidence: 99%

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Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

Kumar

Singh

Khan

et al. 2021

PeerJ Computer Science

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DataStream mining is a challenging task for researchers because of the change in data distribution during classification, known as concept drift. Drift detection algorithms emphasize detecting the drift. The drift detection algorithm needs to be very sensitive to change in data distribution for detecting the maximum number of drifts in the data stream. But highly sensitive drift detectors lead to higher false-positive drift detections. This paper proposed a Drift Detection-based Adaptive Ensemble classifier for sentiment analysis and opinion mining, which uses these false-positive drift detections to benefit and minimize the negative impact of false-positive drift detection signals. The proposed method creates and adds a new classifier to the ensemble whenever a drift happens. A weighting mechanism is implemented, which provides weights to each classifier in the ensemble. The weight of the classifier decides the contribution of each classifier in the final classification results. The experiments are performed using different classification algorithms, and results are evaluated on the accuracy, precision, recall, and F1-measures. The proposed method is also compared with these state-of-the-art methods, OzaBaggingADWINClassifier, Accuracy Weighted Ensemble, Additive Expert Ensemble, Streaming Random Patches, and Adaptive Random Forest Classifier. The results show that the proposed method handles both true positive and false positive drifts efficiently.

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Section: Background and Related Workmentioning

confidence: 99%

“… Ray, Garain & Sarkar (2021) proposed a hotel recommendation system using sentiment analysis of hotel reviews. First, an ensemble of the BERT model was used to find the word embeddings.…”

Section: Background and Related Workmentioning

confidence: 99%

Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

Kumar

Singh

Khan

et al. 2021

PeerJ Computer Science

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“…Ray et al [9] proposed a hotel recommendation system using a sentiment analysis of the hotel reviews and an aspect-based review categorisation that works on the queries given by a user. They provided a new rich and diverse dataset of online hotel reviews crawled from Tripadvisor.com.…”

Section: Literature Surveymentioning

confidence: 99%

JUMRv1: A Sentiment Analysis Dataset for Movie Recommendation

et al. 2021

Self Cite

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Nowadays, we can observe the applications of machine learning in every field, ranging from the quality testing of materials to the building of powerful computer vision tools. One such recent application is the recommendation system, which is a method that suggests products to users based on their preferences. In this paper, our focus is on a specific recommendation system called movie recommendation. Here, we make use of user reviews of movies in order to establish a general outlook about the movie and then use that outlook to recommend that movie to other users. However, a huge number of available reviews has baffled sophisticated review systems. Consequently, there is a need to find a method of extracting meaningful information from the available reviews and use that in classifying a movie review and predicting the sentiment in each one. In a typical scenario, a review can either be positive, negative, or indifferent about a movie. However, the available research articles in the field mainly consider this as a two-class classification problem—positive and negative. The most popular work in this field was performed on Stanford and Rotten Tomatoes datasets, which are somewhat outdated. Our work is based on self-scraped reviews from the IMDB website, and we have annotated the reviews into one of the three classes—positive, negative, and neutral. Our dataset is called JUMRv1—Jadavpur University Movie Recommendation dataset version 1. For the evaluation of JUMRv1, we took an exhaustive approach by testing various combinations of word embeddings, feature selection methods, and classifiers. We also analysed the performance trends, if there were any, and attempted to explain them. Our work sets a benchmark for movie recommendation systems that is based on the newly developed dataset using a three-class sentiment classification.

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“…It can enhance recommendation capabilities (Poria et al, 2016). An ample amount of reviews helps in recommendations (Ray et al, 2021). Reviews can be used with ratings to provide better predictions (Lei et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Recop: Fine-grained Opinions and Collaborative Filtering based Recommender System for Industry 5.0

Bathla

Singh

Kumar

et al. 2021

Preprint

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In the futuristic Industry framework, user interactions with the product are seamlessly integrated with the product life cycle. A recommender system can be considered as an information filtering tool that provides suggestions to users about products, music, friend, topic, etc. This suggestion is based on the interest of users. Several research works have been carried out to improve recommendation accuracy by using matrix factorization, trust-based, hybrid-based, machine learning, and deep learning techniques. However, very few existing works have leveraged textual opinions for the recommendation to the best of our knowledge. Existing research works have focused only on numerical ratings, which do not reflect actual user behaviour. In this research work, sentiments of textual opinions are analyzed for an in-depth analysis of users' behaviour. Furthermore, Natural Language Processing techniques such as lemmatization, stemming, stop-word removal, Part-of-Speech (POS) tagging are applied to textual opinions. Recommendation accuracy is improved by using the proposed score Recop calculated from opinion sentiments. Furthermore, the sparsity issue is resolved by using our proposed approach. Amazon and Yelp review datasets are used for Experiment analysis. Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) values are improved significantly using the proposed approach compared to the existing approaches. MAE and RMSE scores on the Yelp dataset are 0.85 and 1.51, respectively. Additionally, MAE and RMSE scores on the Amazon dataset are 0.66 and 0.93, respectively, significantly contributing to our proposed approach.

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An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews

Cited by 128 publications

References 21 publications

Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

JUMRv1: A Sentiment Analysis Dataset for Movie Recommendation

Recop: Fine-grained Opinions and Collaborative Filtering based Recommender System for Industry 5.0

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