Deceptive Opinions Detection Using New Proposed Arabic Semantic Features

Some users try to post false reviews to promote or to devalue other’s products and services. This action is known as deceptive opinions spam, where spammers try to gain or to profit from posting untruthful reviews. Therefore, we conducted this work to develop and to implement new semantic features to improve the Arabic deception detection. These features were inspired from the study of discourse parse and the rhetoric relations in Arabic. Looking to the importance of the phrase unit in the Arabic language and the grammatical studies, we have analyzed and selected the most used unit markers and relations to calculate the proposed features. These last were used basically to represent the reviews texts in the classification phase. Thus, the most accurate classification technique used in this area which has been proven by several previous works is the Support Vector Machine classifier (SVM). But there is always a lack concerning the Arabic annotated resources specially for deception detection area as it is considered new research area. Therefore, we used the semi supervised SVM to overcome this problem by using the unlabeled data

A weighted-graph based approach for solving the cold start problem in collaborative recommender systems.

International audienceIn recent years, social networks analysis has become a very active area of research. There has been a great focus of studying social networks in the area of graph theory. One important problem in social networks is the identification of the most influential actors which can be detected using different graph parameters. In this paper, we attempt to identify minimum sets of the most influential actors in trust social networks using weighted graphs with more focus on the critical nodes detection. We propose three methods: the first identifies the sets of the most important critical nodes using the concept of network efficiency; the second specifies conditions under which critical nodes should be controlled using connected components; and the third extracts sets of most powerful articulation points which are able to play critical nodes role. Computational results are demonstrated to confirm the effectiveness of our methods

SCOL: Similarity and credibility-based approach for opinion leaders detection in collaborative filtering-based recommender systems

Recommender systems (RSs) have recently gained significant attention from both research and industrial communities. These systems generate the recommendations of items in one of two ways, namely collaborative or content-based filtering. Collaborative filtering is a technique used by recommender systems in order to suggest to the user a set of items based on the opinions of other users who share with him the same preferences. One of the key issues in collaborative filtering systems (CFSs) is how to generate adequate recommendations for newcomers who rate only a small number of items, a problem known as cold start user. Another interesting problem is the cold start item when a new item is introduced in the system and cannot be recommended. In this paper, we present a clustering-based approach SCOL that aims to alleviate the cold start challenges; by identifying the most effective opinion leaders among the social network of the CFS. SCOL clustering focuses on the credibility and correlation similarity concepts.1201345

Recommender System Through Sentiment Analysis

International audience—Customer product reviews play an important role in the customer's decision to purchase a product or use a service. Customer preferences and opinions are affected by other customers' reviews online, on blogs or over social networking platforms. We propose a multilingual recommender system based on sentiment analysis to help Algerian users decide on products, restaurants, movies and other services using online product reviews. The main goal of this work is to combine both recommendation system and sentiment analysis in order to generate the most accurate recommendations for users. Because both domains suffer from the lack of labeled data, to overcome that, this paper detects the opinions polarity score using the semi-supervised SVM. The experimental results suggested very high precision and a recall of 100%. The results analysis evaluation provides interesting findings on the impact of integrating sentiment analysis into a recommendation technique based on collaborative filtering

Deceptive Opinions Detection Using New Proposed Arabic Semantic Features

International audienceSome users try to post false reviews to promote or to devalue other's products and services. This action is known as deceptive opinions spam, where spammers try to gain or to profit from posting untruthful reviews. Therefore, we conducted this work to develop and to implement new semantic features to improve the Arabic deception detection. These features were inspired from the study of discourse parse and the rhetoric relations in Arabic. Looking to the importance of the phrase unit in the Arabic language and the grammatical studies, we have analyzed and selected the most used unit markers and relations to calculate the proposed features. These last were used basically to represent the reviews texts in the classification phase. Thus, the most accurate classification technique used in this area which has been proven by several previous works is the Support Vector Machine classifier (SVM). But there is always a lack concerning the Arabic annotated resources specially for deception detection area as it is considered new research area. Therefore, we used the semi supervised SVM to overcome this problem by using the unlabeled data