Recently, the approach towards mining various opinions on weblogs, forums and websites has gained attentions and interests of numerous researchers. In this regard, feature-based opinion mining has been extensively studied in English documents in order to identify implicit and explicit product features and relevant opinions. However, in case of texts written in Persian language, this task faces serious challenges. The objective of this research is to present an unsupervised method for feature-based opinion mining in Persian; an approach which does not require a labeled training dataset. The proposed method in this paper involves extracting explicit product features. Previous studies dealing with extraction of explicit features often focus on lexical roles of words; the approach which cannot be used in distinguishing between an adjective as a part of a noun or a sentiment word. In this study, in addition to lexical roles, syntactic roles are also considered to extract more relevant explicit features. The results demonstrate that the proposed method has got higher recall and precision values compared to prior studies.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

The present paper discusses implicit memory (compared to explicit memory) as a new phenomenon in memory research. First, ideas about implicit memory are considered very briefly from historical point of view. Then, as in recent studies, two lines of research are discussed; i.e., subliminal stimuli and repetition priming studies. These studies, especially priming studies, show that explicit memory are affected differently by various variables. While explicit memory tests are affected by the levels-of-processing and the interval between encoding and retrieval stages (forgetting effect), implicit memory tests are not affected. On the other hand, while implicit memory tests are sensitive to modality and mood changes, manipulated both at encoding and at retrieval, memory tests are unaffected. The findings related to amnesic patients are impaired with respect to explicit memory and are intact with respect to implicit memory.There are two theoretical explanations for this double dissociation between explicit and implicit memory. The processing view suggests that there is a single and integrated memory system and the discrepancy between explicit and implicit memory are explained the different natures of relation between encoding and retrieval processes or procedures. The multiple memory systems view, which is more promising and supported by empirical evidence, claims that each of these two types of memory is supported by an independent and separate system(s). Although these systems interact with each other, they follow different rules, however.

Typically, when someone wants to buy an online product, he/she reviews the comments and notes written by others about the product. Clearly, it has a profound impact on the person's decision to buy or not to buy the product. Sentiment analysis or opinion mining is one of the hot research topics in computer science. The main purpose of sentiment analysis is to extract opinions of individuals about the characteristics of an entity such as a product. In this research, an unsupervised opinion mining is proposed for Persian products based on implicit feature extraction which is a critical step in sentiment analysis. In most previous studies, statistical information is utilized to create a co-occurrence matrix and determine the implicit features. In this paper, we benefit from syntactic rules and sentiment clustering in conjunction with statistical information to construct an efficient co-occurrence matrix between features and sentiment words. The evaluation results provided on a real-world dataset, extracted from Digikala website, indicates that the proposed method achieves higher recall and precision compared to the previous studies.