A Novel Approach to Recommender System Based on Aspect-level Sentiment Analysis

Traditional recommendation methods usually focus on utilizing user’s features obtained from structured behavior information, which only contains coarse grained user interests. This paper presents a novel approach to introduce aspect-based sentiment analysis (or opinion mining) into recommender systems. By extracting aspects from the user’s review text and determining the sentiment orientation of each aspect, we build the user and product model focusing on mining user’s interests and the practical evaluations about the product. Experimental results show that the recommendation utilizing this model conduces to better performance than common traditional recommendation methods. Introduction Recommender systems, with an aim to provide personalized suggestions of products to the particular users, have been wildly used in practical applications [1]. Traditional recommendation methods usually focus on utilizing user profiles obtained from their past purchasing behaviors, from which the similarities [2] or relationships [3] between each user are generated. However, these structured data, such as purchasing behaviors or ratings range from 1 to 5, only express the coarsest grained sentiment orientations that hardly reflect the details of user interests or his attitudes towards each part of the product. For example, a positive rating (4 or 5) about the entity does not mean that the author has positive opinions about all aspects of the entity. Likewise, a negative rating (1 or 2) document does not mean that the author is negative about everything. Compared with the structured information, user review, as a natural language text in unstructured format, contains much finer grained sentiment orientations towards different aspects of a single item. To obtain the detail of author’s opinion, we need to discover the aspects of this comment and determine whether the sentiment is positive or negative or the extent of positive/negative attitude on each aspect [4]. These opinions of various aspects of the product can be utilized to construct the user model as well as the product model. On the one hand, all unique aspects, which extracted from all comments made about different items by a particular user, form a list of aspects which opinion holder takes interested in. On the other hand, all comments about one particular product made by different people determine the general evaluation of this product. For example, here is a typical restaurant comment, “The ambiance is great and the stuff is friendly. ” The two aspects, ambiance and stuff, is definitely the aspects this user interested in, so that the customer would evaluates other restaurants on the same two aspects with a high probability. Meanwhile, the combination of aspects of various comments and their sentiment orientation (degree of positive or negative), to some extent, reflect a comprehensive assessment on different parts of this restaurant. In this paper, we propose a novel approach to introduce aspect-based sentiment analysis into recommender systems. We extract aspect of the product using the topic model and then identify aspect-specific sentiment words using the SentiWordNet (a sentiment lexicon). We then use the result of sentiment analysis to make user interests model and the product model. By comparing two models of each user-product pair, we obtain the similarity of the user’s interest and the product. A modification of traditional method based on this model conduces to much better performance. 4th National Conference on Electrical, Electronics and Computer Engineering (NCEECE 2015) © 2016. The authors Published by Atlantis Press 1453 Related work There have been a number of efforts focused on sentiment analysis. Pang et al. [5] first use supervised learning approaches to classify movie reviews into two classes. To extract the aspects of item and determine sentiment orientation of each aspect, Hu and Liu [6] propose a method aiming to discover aspects by finding frequent nouns and noun phrases. The occurrence of nouns and noun phrases (or groups), identified by a part-of-speech (POS) tagger, are counted and only the frequent ones are kept in their study. Popescu et al. [7] improve this method by removing the noun phrases that may not be aspects of entities using a pointwise mutual information (PMI) score. Such method works effectively in some situations. It doesn’t consider the semantic similarity of each word however. In other words, this method cannot classify two similar word (e.g., atmosphere/ambience, staff/waiter) into one aspect class. Similar to other natural language processing(NLP) problem, many supervised learning algorithms, such as hidden Markov models (HMM) and conditional random fields (CRF), have been proposed for this information extraction task [8]. Jin and Ho [9] applied a lexicalized HMM model to learn patterns to extract aspects and opinion expressions. Since these are supervised techniques, sufficient amount of labeled data for training is needed. To make things worse, which is different from other NLP tasks,one need manually annotate aspects and non-aspects in a corpus and make labeled data cover enough aspects to make sure it works well on complex aspect. To solve the issues above, a series of methods using topic model have been proposed. Brody and Elhadad [10] proposed an unsupervised learning method using topic model to identify aspects and then identify aspect-specific sentiment words by considering adjectives only. In this paper, we improve this method by utilizing the sentiment lexicon to identify the aspect-specific sentiment words. This method doesn’t acquire manually labeled data and works well on semantically similar words which means different words expressing the same or related property are automatically grouped together under the same aspect. Zhang et al. [11] propose a user modeling method by evaluating the user’s experience and skill. They then extract aspects of product and model the product with two sentiment orientation class of good or bad. They neglect the evaluations about the review by other user as well as the fine grained sentiment orientation of the opinion holder.