In recent times, the recommender systems (RSs) have considerable importance in academia, commercial activities, and industry. They are widely used in various domains such as shopping (Amazon), music (Pandora), movies (Netflix), travel (TripAdvisor), restaurant (Yelp), people (Facebook), and articles (TED). Most of the RSs approaches rely on a single-criterion rating (overall rating) as a primary source for the recommendation process. However, the overall rating is not enough to gain high accuracy of recommendations because the overall rating cannot express fine-grained analysis behind the user’s behavior. To solve this problem, multi-criteria recommender systems (MCRSs) have been developed to improve the accuracy of the RS performance. Additionally, a new source of information represented by the user-generated reviews is incorporated in the recommendation process because of the rich and numerous information included (i.e. review elements) related to the whole item or to a certain feature of the item or the user’s preferences. The valuable review elements are extracted using either text mining or sentiment analysis. MCRSs benefit from the review elements of the user-generated reviews in building their criteria forming multi-criteria review based recommender systems. The review elements improve the accuracy of the RS performance and mitigate most of the RS’s problems such as the cold start and sparsity. In this review, we focused on the multi-criteria review-based recommender system and explained the user reviews elements in detail and how these can be integrated into the RSs to help develop their criteria to enhance the RSs performance. Finally, based on the survey, we presented four future trends based on this type of RSs to support researchers who wish to pursue studies in this area.

Aspect-based sentiment analysis (ABSA) has recently attracted increasing attention due to its extensive applications. Most of the existing ABSA methods been applied on small-sized labeled datasets. However, real datasets such as the Amazon and TripAdvisor contain a massive number of reviews. Thus, applying these methods on large-scale datasets may produce inefficient results. Furthermore, these existing methods extract huge number of aspects, most of which are not relevant to the domain of interest. But, on other hand, some of the infrequent relevant aspects are excluded during the extraction process. These limitations negatively affect the performance of the ABSA process. This paper, therefore, aims to overcome such limitations by proposing an efficient approach that is suitable for real large-scale unlabeled datasets. The proposed approach is a combination of hybridizing a frequency-based approach (word level) and a syntactic-relation based approach (sentence level). It was enhanced further with a semantic similarity-based approach to extract aspects that are relevant to the domain, even terms (related to the aspects) are not frequently mentioned in the reviews. The extracted aspects according to the proposed approach are used to generate a total review sentiment score after estimating the weight and the rating of each extracted aspect mentioned in the review. The assignment of the weight of each extracted aspect is calculated based on a modified TF-IDF weighting scheme and the assignment of the aspect rating is calculated based on a domain-specific lexicon. Effectiveness of the extracted aspects is evaluated against two baselines available from existing literature: fixed aspect and extracted aspects. Evaluation was also performed by using a general lexicon and a domain-specific lexicon. Results in terms of F-measure and accuracy on Amazon and Yelp datasets show that the extracted aspects using the proposed approach with the domain-specific lexicon outperformed all the baselines.

With the emergence of microblogging platforms and social media applications, large amounts of user-generated data in the form of comments, reviews, and brief text messages are produced every day. Microblog data is typically of poor quality; hence improving the quality of the data is a significant scientific and practical challenge. In spite of the relevance of the problem, there has been not much work so far, especially in regard to microblog data quality for Short-Text Topic Modelling (STTM) purposes. This paper addresses this problem and proposes an approach called the social media data cleansing model (SMDCM) to improve data quality for STTM. We evaluate SMDCM using six topic modelling methods, namely the Latent Dirichlet Allocation (LDA), Word-Network Topic Model (WNTM), Pseudo-document-based Topic Modelling (PTM), Biterm Topic Model (BTM), Global and Local word embedding-based Topic Modeling (GLTM), and Fuzzy Topic modelling (FTM). We used the Real-world Cyberbullying Twitter (RW-CB-Twitter) and the Cyberbullying Mendeley (CB-MNDLY) datasets in the evaluation. The results proved the efficiency of the GLTM and WNTM over the other STTM models when applying the SMDCM techniques, which achieved optimum topic coherence and high accuracy values on RW-CB-Twitter and CB-MNDLY datasets.

The linguistics related research and particularly, sentiment analysis using data-driven approaches, has been growing in recent years. However, the large number of users and excessive amount of information available on social media, make it difficult to detect extremism text on these platforms. The literature revealed a plethora of research studies focusing the sentiment analysis primarily, for English texts, however, very limited studies are available concerning the Arabic language which is the 4th mostly spoken language in the world. We first time in this study, propose a text detection mechanism for extremism orientations distinction in Arabic language, to improve the comprehension of subjective phrases. The study introduces a novel method based on Rough Set theory to enhance the accuracy of selected models and recognize text orientation reliably. Experimental outcomes indicate that the proposed method outperforms existing algorithms by contributing towards feature discriminations. Our method achieved 90.853%, 81.707% and 71.951% accuracies for unigram, bigram, and trigram representations, respectively. This study significantly contributes to the limited research in the field of machine learning and linguistics in Arabic language.

Collaborative filtering (CF) approaches generate user recommendations based on user similarities. These similarities are calculated based on the overall (explicit) user ratings. However, in some domains, such ratings may be sparse or unavailable. User reviews can play a significant role in such cases, as implicit ratings can be derived from the reviews using sentiment analysis, a natural language processing technique. However, most current studies calculate the implicit ratings by simply aggregating the scores of all sentiment words appearing in reviews and, thus, ignoring the elements of sentiment degrees and aspects of user reviews. This study addresses this issue by calculating the implicit rating differently, leveraging the rich information in user reviews by using both sentiment words and aspect–sentiment word pairs to enhance the CF performance. It proposes four methods to calculate the implicit ratings on large-scale datasets: the first considers the degree of sentiment words, while the second exploits the aspects by extracting aspect-sentiment word pairs to calculate the implicit ratings. The remaining two methods combine explicit ratings with the implicit ratings generated by the first two methods. The generated ratings are then incorporated into different CF rating prediction algorithms to evaluate their effectiveness in enhancing the CF performance. Evaluative experiments of the proposed methods are conducted on two large-scale datasets: Amazon and Yelp. Results of the experiments show that the proposed ratings improved the accuracy of CF rating prediction algorithms and outperformed the explicit ratings in terms of three predictive accuracy metrics.

Collaborative filtering (CF) is one of the most widely utilised approaches in recommendation techniques. It suggests items to users based on the ratings of other users who share their preferences. Thus, one of the aims of CF is to find reliable neighbours. Typically, CF produces a sparse user-item rating matrix, when relying only on the ratings to identify the precise neighbours, resulting in poor performance. User reviews can be essential in overcoming those situations because of the diverse elements available in reviews. The most popular element is aspects, which can provide a fine-grained analysis of users’ behaviours, thus improving personalised recommendations. However, increasing the number of aspects also results in sparsity, therefore may deteriorate the recommendation performance. As a result, clustering of aspects may lessen this sparsity, but it is yet unclear how much this would affect the performance of CF systems. This study proposes a CF approach based on aspect clustering that addresses the above issue in terms of rating prediction. The approach aims to reduce the sparseness in the multi-criteria rating matrix by grouping aspects into clusters based on their semantic similarity, which will be less expensive and require less memory to discover the neighbourhood set. Our approach extracts aspects and represents them using Google’s pre-trained Word2vec model. Then, aspects are organised into clusters using the K-means clustering algorithm. Multi-dimensional Euclidean distance is used as a similarity measure for finding the appropriate neighbours and predicted ratings of unseen items are then made using the kNN algorithm. This study also identifies the number of aspects that significantly impacts CF performance. Experiments are carried out using a real large-scale dataset: the Amazon movie dataset. Evaluation is also performed by comparing CF performance of the proposed approach with three different baseline approaches. Results show that the proposed approach improves CF performance compared to other approaches in terms of three predictive accuracy metrics.