Combining RSS-SVM with genetic algorithm for Arabic opinions analysis

Copyright © 2019 Inderscience Enterprises Ltd. Due to the large-scale users of the Arabic language, researchers are drawn to the Arabic sentiment analysis and precisely the classification areas. Thus, the most accurate classification technique used in this area is the support vector machine (SVM) classifier. This last, is able to increase the rates in opinion mining but with use of very small number of features. Hence, reducing feature’s vector can alternate the system performance by deleting some pertinent ones. To overcome these two constraints, our idea is to use random sub space (RSS) algorithm to generate several features vectors with limited size; and to replace the decision tree base classifier of RSS with SVM. Later, another proposition was implemented in order to enhance the previous algorithm by using the genetic algorithm as subset features generator based on correlation criteria to eliminate the random choice used by RSS and to prevent the use of incoherent features subsets

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

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