With the explosive growth of social media such as Twitter and Instagram, reviews on e-commerce websites, and comments on news websites, individuals and organizations are increasingly using analyzing opinions in these media for their decision-making and designing strategies. Sentiment analysis is one of the techniques used to analyze users' opinions in recent years. The Persian language has specific features and thereby requires unique methods and models to be adopted for Sentiment analysis, which are different from those in English and other languages. This paper identifies the characteristics and limitations of the Persian language. Sentiment analysis in each language has specified prerequisites, hence, the direct use of methods, tools, and Resources developed for the English language in Persian has its limitations. The present study aims to investigate and compare previous Sentiment analysis studies on Persian texts and describe views presented in articles published in the last decade. First, the Sentiment analysis levels, approaches, and tasks are described. Then, a detailed survey of the applied Sentiment analysis methods used for Persian texts is presented, and previous works in this field are discussed. The advantages and disadvantages of each proposed method are demonstrated. Moreover, the publicly available Sentiment analysis Resources of Persian texts are studied, and the characteristics and differences of each are highlighted. As a result, according to the recent development of the Sentiment analysis field, some issues and challenges not being addressed in Persian texts are listed, and some guidelines are provided for future research on Persian texts. Future requirements of Persian text for improving the Sentiment analysis system are detailed.

Sentiment analysis is the process of analyzing a person’ s perception or belief about a particular subject matter. However, finding correct opinion or interest from multi-facet Sentiment Data is a tedious task. In this paper, a method to improve the Sentiment accuracy by utilizing the concept of categorized dictionary for Sentiment classification and analysis is proposed. A categorized dictionary is developed for the Sentiment classification and further calculation of Sentiment accuracy. The concept of categorized dictionary involves the creation of dictionaries for different categories making the comparisons specific. The categorized dictionary includes words defining the positive and negative Sentiments related to the particular category. It is used by the mapper reducer algorithm for the classification of Sentiments. The Data is collected from social networking site and is pre-processed. Since the amount of Data is enormous therefore a reliable open-source framework Hadoop is used for the implementation. Hadoop hosts various software utilities to inspect and process any type of big Data. The comparative analysis presented in this paper proves the worthiness of the proposed method.

With the advent of digital technology and access to huge volume, speed and variety of Data, big Data analytics has become an inevitable thing in human Resource management. Although research has paid increasing attention to Data analytics in human Resource management, and researchers have realized the importance of using a big Data-based approach in human Resource management, there is still a gap in this research area. Therefore, due to the growing interest in this field, there is a need for a complete analysis of the structure and development of this research topic. Hence, the aim of this study is to provide an in-depth understanding of big Data analytics research in human Resource management that evaluates past and present trends through bibliometric analysis of existing research. The findings from the analysis provide knowledge structure and mapping research streams of Data analytics in human Resource management. Therefore, in addition to enriching the literature of the big Data-based approach in human Resource management by integrating bibliometric analysis, the present study contributes to a better understanding of researchers in this research field by identifying the patterns in the current literature.

Sentiment Analysis or opinion mining is NLP's method to computationally identify and categorize user opinions expressed in textual Data. Mainly it is used to determine the user's opinions, emotions, appraisals, or judgments towards a specific event, topic, product, etc. is positive, negative, or neutral. In this approach, a huge amount of digital Data generated online from blogs and social media websites is gathered and analyzed to discover the insights and help make business decisions. Social media is web-based applications that are designed and developed to allow people to share digital content in real-time quickly and efficiently. Many people define social media as apps on their Smartphone or tablet, but the truth is, this communication tool started with computers. It became an essential and inseparable part of human life. Most business uses social media to market products, promote brands, and connect to current customers and foster new business. Online social media Data is pervasive. It allows people to post their opinions and Sentiments about products, events, and other people in the form of short text messages. For example, Twitter is an online social networking service where users post and interact with short messages, called "tweets. " Hence, currently, social media has become a prospective source for businesses to discover people's Sentiments and opinions about a particular event or product. This paper focuses on the development of a Multinomial Naï ve Bayes Based social media Data emotion analyzer and Sentiment classifier. This paper also explains various enriched methods used in pre-processing techniques. This paper also focuses on various Machine Learning Techniques and steps to use the text classifier and different types of language models.

The performance and Resources allocation in large organizations such as banks, universities, and airports are one of the most important indicators in organizational management science. In this paper, by Data envelopment analysis, which is a very powerful method of evaluating the efficiency of organizations, we analyze and review the performance and Resource allocation. The optimal allocation of Resources in organizations is considered to be the most important tool for implementing a long-term strategy and program for them, and the policies and objectives of the organization's plan are reflected in Resources allocation of the activities. Indeed, given the importance of future organizations' performance, managers, taking into account the efficiency of each unit, provide strategies for target setting and how to allocate Resources, including human Resources, financial costs, technological facilities, and so on. On the other hand, given that actual Data in organizations are usually random and stochastic, this paper addresses methods for allocating Resources with stochastic Data. Also, in line with this research, we will devise strategies for allocating Resources as well as confronting limited Resources in stochastic Data, which will result in a new model for Data envelopment analysis. In this model, stochastic Data are presented with probability distribution due to probability. One of the most valuable achievements in this paper is to resolve the problem of allocating appropriate and optimal limited Resources with stochastic Data. Finally, with numerical results, the advantages of the new model are shown in relation to the previous models with stochastic Data.