درخت حوزه‌های تخصصی

State banks must find a way to adopt a different strategy from other banks in providing customer service. An important and key way to create a competitive position is to differentiate services from other competitors that create a good image of the bank and its services in the minds of customers. A topic that has been addressed in the present study is the necessity for state banks, like other companies and businesses, to follow a suitable model in order to achieve a suitable position in the banking industry. In this regard, the purpose of this study is to design a model for positioning the services of state-owned banks and its validation. The data for qualitative stage were obtained from Keshavarzi, Melli, and Maskan branches' managers of Khorasan Razavi province and university professors in the field of management. The quantitative sample were also the customers of aforementioned banks, that due to their large number, 384 people from each bank were selected as a sample size by simple random method and Cochran's formula. In order to collect data in the qualitative part, Deleted part, the interview was used and in the quantitative part, a researcher-made questionnaire was used, which obtained validity with expert opinions and reliability with Cronbach's alpha of 0.77. In order to analyze the data in the qualitative part, grounded theory and the method of Strauss and Corbin and Maxquda software were used, and in the quantitative part, the structural equation technique and PLS2 software were used. The results showed that the model presented in 6 dimensions of causal factors,contextual, intervening, pivotal, strategies an consequences has a good fit.

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 Naïve 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.