درخت حوزه‌های تخصصی

World of technology provides everyone with a great outlet to give their opinion, using social media like Twitter and other platforms. This paper employs machine learning methods for text analysis to obtain sentiments of reviews by the people on twitter. Sentiment analysis of the text uses Natural language processing, a machine learning technique to tell the orientation of opinion of a piece of text. This system extracts attributes from the piece of writing such as a) The polarity of text, whether the speaker is criticizing or appreciating, b) The topic of discussion, subject of the text. A comparison of the work done so far on sentiment analysis on tweets has been shown. A detailed discussion on feature extraction and feature representation is provided. Comparison of six classifiers: Naïve Bayes, Decision Tree, Logistic Regression, Support Vector Machine, XGBoost and Random Forest, based on their accuracy depending upon type of feature, is shown. Moreover, this paper also provides sentiment analysis of political views and public opinion on lockdown in India. Tweets with ‘#lockdown’ are analysed for their sentiment categorically and a schematic analysis is shown.

Usually, in real-world engineering problems, there are different types of uncertainties about the studied variables, which can be due to the specific variables under investigation or interaction between them. Fuzzy cognitive maps, which addresses the cause-effect relation between variables, is one of the most common models for better understanding of the problems, especially when the quantitative data are not available or the nature of the problem is qualitative. One of the significant issues of automatic construction of fuzzy cognitive maps is that it does not consider the experts' uncertainties because of their cognitive attitudes such as age, experience, etc., which affects the quality and validity of modeling of the complex problems. Therefore, in this paper, a Z-Cognitive maps approach is proposed based on the distance-based automatic construction approach and Z-numbers to capture uncertainty and ambiguity of experts' opinions on the variables and causality relationships. The suggested approach can act as decision support for cognitive maps problems considering the experts' cognitive information, which is tested by a numerical example.

Moral intelligence is one of the most important factors that can increase effectiveness and efficiency, the two important factors that every organization wants to survive in a chaotic market and should concentrate more on them. In general, when researchers want to promote moral intelligence they study its effects on other related issues like educational effectiveness. This paper investigates moral intelligence and effectiveness in education.

The goal of this study is to examine the antecedents of brand citizenship behavior in financial institutions by considering the internal relationship between them. The research method is descriptive and applied in terms of purpose and in terms of data collection method is field. The data collection tool in this study was a questionnaire. In this study, the statistical population was the employees of private and public banks and financial institutions in Bandar Abbas. The sampling method was cluster-random. After placing the banks in the three clusters of state-owned banks, private banks and financial and credit institutions, Sepah, Sanat and Ma'dan banks were randomly selected from among the state-owned banks. Ansar Cooperative, Ayandeh, Shahr, Refah and Mehr Eghtesad Banks were randomly selected from among private banks and Noor and Caspian Financial and Credit Institutions were also randomly selected from among financial and credit institutions. A total of 213 questionnaires were distributed among the customers of selected banks as convenient, and finally 203 questionnaires could be reviewed. Data analysis was performed by SPSS16 and PLS3-SMART software. The results show that brand-centered human resource management and brand identification have a positive and significant effect on brand citizenship behavior, but a positive relationship between brand trust, brand commitment, employees' self-concept of brand and brand pride with brand citizenship behavior was not found. Also, a positive relationship between brand-centered human resource management and employees' self-concept of brand, employees' self-concept of brand and brand pride, employees' self-concept of brand and brand identification, brand identification and brand pride, brand identification and brand commitment, and brand trust and brand commitment were confirmed.