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

Crystal structure of 3-[4-(1-methylethyl)phenyl]-1-(naphthalen-2-yl)prop-2-en-1-one

The title compound, C22H20O, was synthesized by reacting 4-isopropylbenzaldehyde with 2-acetonaphtone by aldolic condensation under Claisen–Schmidt conditions. The molecule consists of a naphthalene group and a benzene ring with a pendant isopropyl moiety, both rings bound by a propenone linker. The naphthalene ring system is almost planar [maximum deviation from the least-squares plane = 0.026 (10) Å] and subtends a dihedral angle of 52.31 (4)° with the benzene ring. The propenone linker, in turn, deviates slightly more from planarity [maximum deviation = 0.125 (18) Å] and has its least-squares plane oriented midway the former two, at 25.62 (6) and 28.02 (5)° from the naphthalene ring system and the benzene ring, respectively. Finally, the isopropyl group presents its CC2 plane almost perpendicular to the benzene ring, at 85.30 (4)°. No significant hydrogen bonding or π–π stacking interactions are found in the crystal structure

Particle Swarm Optimization Based Swarm Intelligence for Active Learning Improvement: Application on Medical Data Classification

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Semi-supervised learning targets the common situation where labeled data are scarce but unlabeled data are abundant. It uses unlabeled data to help supervised learning tasks. In practice, it may make sense to utilize active learning in conjunction with semi-supervised learning. That is, we might allow the learning algorithm to pick a set of unlabeled instances to be labeled by a domain expert, which will then be used as the labeled data set. However, existing approaches are computationally expensive and require searching through an entire unlabeled dataset, which may contain redundant instances that provide no instructive information to the classifier and can decrease the performance. To address this optimization problem, a hybrid system that combines active learning (AL) and particle swarm optimization (PSO) algorithms is proposed to reduce the cost of labeling while building a more efficient classifier. The novelty of this work resides in the integration of a bio-inspired optimization algorithm in the machine learning strategy. Furthermore, a novel uncertainty measure was integrated into the particle swarm optimization algorithm as an objective function to select from massive amounts of medical instances those that are deemed most informative. To evaluate the effectiveness of the proposed approach, eighteen (18) benchmark datasets were used and compared against three best-known classifiers with different learning paradigms: AL–NB an active learning algorithm using Naïve Base classifier and Margin Sampling strategy, SVM (Support Vector Machine), ELM (Extreme Learning Machine) with supervised learning, and TSVM (Transductive Support Vector Machine) with the semi-supervised learning. Experiments showed that the proposed approach is effective in reducing the efforts required by experts for medical data annotation to produce an accurate classifier. The active learning approach has been utilized to optimize the expensive task of labeling. Based on a novel uncertainty measure, the nature-inspired algorithm PSO attempts to select from massive amounts of unlabeled medical instances those considered informative, at the same time improving the classifier performance. The experiments carried out confirm that the proposed strategy significantly enhances the performance of the AL algorithm compared with the commonly used uncertainty strategies. It achieves a performance similar to that of fully supervised and semi-supervised algorithms while requiring much less labeling. As a future extension of this work, it would be interesting to integrate other evolutionary optimization algorithms and compare them with our approach. In addition, it is beneficial to test the impact of using other variants of PSO algorithm in our approach. Also, it is aimed to test more classification algorithms in the experimentation process

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