Prediction of Opinion Keywords and Their Sentiment Strength Score Using Latent Space Learning Methods  
[PERSON]  
1 Science, Computing, and Technology Department, School of Architecture, Engineering and Design, Universidad Europea de Madrid, Calle Tajo, S/N, Villaviciosa de Odon, 28670 Madrid, Spain; [EMAIL_ADDRESS] (D.G.-V.); [EMAIL_ADDRESS] (L.G.-E.); [EMAIL_ADDRESS] (J.M.L.-L.)  
2 Data Science Laboratory, Universidad Europea de Madrid, Calle Tajo, S/N, Villaviciosa de Odon, 28670 Madrid, Spain
the website models how the assessments are done to recommend new products they may be interested in [1], or to identify users of similar taste [2]. Most existing recommendation systems fit into one of the following two categories: (_i_) content-based recommendation or (_ii_) collaborative filtering (CF) systems [3]. The first approach defines the users profile that best represents all the gathered personal information (such as tags, keywords, text comments, and likes/dislikes [4]) and recommends items based on each individual characteristics. These recommendations are then generated by comparing items with user profiles [4]. The main benefit of this approach is the simplicity and ease of interpretation of the recommendations it provides. One of its main drawbacks is the fact that it ignores user opinions on different products, taking only their preferences into account. Figure 1 shows the metadata of a real text review that includes the user's opinion. There are several works that try to solve this limitation by including the text features of the user's reviews (e.g., frequencies of occurrence of words) into the model. In [5] the authors incorporate reviews, items, and text features into a three-dimensional tensor description to unveil the different sentiment effects that arise when the same word is used by different users in ranking different items. These improvements lead to better ranking predictions when compared to previous models. Also, ref. [6] presents an extension of the user-\(kNN\) algorithm that uses the similarities between text reviews to gather the similarity between users, outperforming the conventional algorithms that only use the ratings as inputs. On the other hand, the CF category has achieved the most successful results as shown in the Netflix Prize Challenge [7; 8]. This method uses the similarities among users to discover the latent model which best describes them and retrieves predicted rankings for specific items. Some modifications have been later proposed to properly address the negative latent factors [9; 10] as well as to gain interpretability [11]. In this last reference, authors present a hidden factor model to understand why any two users may agree when reviewing a movie yet disagree when reviewing another: The fact that users may have similar preferences towards one genre, but opposite preferences for another turns out to be of primary importance in this context. These same authors also propose in [1] the use of the latent factors to achieve a better understanding of the connection between the rating dimensions and the intrinsic features of users and their tastes. During the last decade, the proliferation of social media has gone hand in hand with the analysis of user opinions and sentiments [12], which has been applied successfully in a variety of fields such as social networks [13] or movies [14]. In [15] authors use text reviews to describe user interests and sentiments, hence improving the results obtained in the prediction of the ratings. These text reviews are also used [16; 17] to guide the learned latent space by combining content-based filtering with collaborative filtering techniques. However, being capable of predicting the opinion or sentiment associated with non-existent reviews remains an open question that deserves further attention [18]. This article in particular focuses on improving the current recommendation systems by predicting the sentiment strength scores for a series of opinion keywords that describe each item.