Background: Anti-immigrant sentiments are macro-level determinants and may explain why immigrants are treated poorly in some host countries. However, very few studies test whether anti-immigrant sentiments at the macro level are associated with individual-level outcomes of immigrants, such as their happiness. This is important because, according to marginalization-related diminished returns (MDRs) theory, immigrants show worse-than-expected outcomes because of reduced effects of socioeconomic resources. Aim: Built on a multi-level model of happiness and MDRs theory,we conducted this study to test the association between country-level anti-immigrant sentiments and individual-level happiness of immigrants across European countries. Methods: For this cross-sectional study, we borrowed data from European Social Survey 2020 (ESS 2020). Participants were recruited from Bulgaria, Czechia, Estonia, Finland, France, Croatia, Hungary, Lithuania, Slovenia, and Slovakia. Participants included 997 immigrants. Age, sex, education, employment (worked in the past week), and self-rated health were control variables. Level of countrylevel anti-immigrant sentiments was the independent variable. Happiness was the outcome. Linear regression was used for data analysis. Results: There were considerable country-level variations in anti-immigrant sentiment across European countries. There were also considerable variations in immigrants' happiness across countries. Overall, higher levels of anti-immigrant sentiments in the country were associated with lower levels of immigrants' happiness. The results remained similar without and with control variables, including age, gender, education, employment, and self-rated health. Conclusion: Higher anti-immigrant sentiments, at the macro level, are associated with a lower level of happiness among immigrants at the individual level. Research should test if reduced opportunities or worse treatment of immigrants explains why country-level anti-immigrant sentiments contribute to reduced happiness of immigrants.

According to the traditional view about stock return, stock price changes are due to systematic changes in the fundamental values of the firm. However, recent researches suggested that the investor sentiment plays a key role in price determination and explaining time series yields, especially for those stocks which valuations are highly subjective and difficult to arbitrage.This research investigates the impact of the investor sentiments on stock return of selected portfolios which were sorted by different parameters such as size, price, book-to-market value ratio and institutional ownership ratio during 2002 to 2010. Monthly return for each portfolio was calculated based on monthly return of each company by using equal weight strategy. Finally, the coefficients were estimated by using multivariable regression method on the model as time series. For this purpose, the four factor model of portfolio of market, the firm size, book-market-value ratio and investor sentiment were used in order to explain the return. According to the results of the study, there is a positive meaningful relationship between investor sentiments and stock return of the companies which have minimum size, book-market-value ratio and institutional ownership ratio.

«Adolphe», Benjamin Constant's novel, is highly respected in literary history of France. This book that summarizes the features of two great literary schools, namely, Classicism and Romanticism, is a perfect manifestation of Psychological novel. In «Adolphe», Benjamin Constant describes the bitter truths residing in human heart with a unique art and great exactitude and fineness. These truths have existed in all the times and places. With a thoroughgoing analysis of sentiments and psychological aspects of his heros, he reveals to the readers their weakness and strengths, and advises them indirectly. The present article, that is a descriptive- analytic one, deals with psychological analysis in «Adolphe». This is done in three various ways: through indirect speeches, dialogues and monologues. By demonstrating a different conception of love and by psychological analysis of thoughts, sentiments, and affections of his heros, Constant tries to tell that man is egoist, weak, unstable and very tempestuous.

In the present study, managerial sentiment refers to the pleasant and optimistic feeling of the managers and decision makers in the companies. Optimism can be considered a spiritual state showing ones hope for the future. Talking about a company, it is the manger’ s hope for future of the company. Accordingly, the present study examines effects of the managerial optimism on the relationship between investment and cash flow in the company, as well as the moderating effect of financial constraints on this relationship. To do so, we used data of 150 companies listed in Tehran Stock Exchange from 2011 to 2015, multiple regression, as well as panel data model. The results suggested that the manager’ s optimism cannot have a significant effect on the investment-cash flow relationship; and the financial constraint enhances and signifies the effect of manager’ s optimism on the investment-cash flow relationship.

Because of the non-linear fluctuations of the stock price, it is challenging to predict. Therefore, it is important to identify the characteristics that can be used to predict market behavior. Both traditional and modern methods are very important for these features. Among these features, we can mention the feelings and emotions of consumers. In this research, the effect of consumer sentiment factor in predicting market fluctuations has been investigated. For this purpose, the S&P500 index has been considered for forecasting first without considering the characteristics of consumer sentiments using BiLSTM deep neural network and then by combining the UMCSENT consumer sentiment factor. The experiments and results of this research show that the use of consumers' feelings and emotions increases the accuracy of forecasting the S&P500 index.

In this study, a comprehensive meta-analysis was conducted using a random-effects model to examine the relationship between money supply and inflation and to address inconsistencies in previous research. The overall effect of money supply on inflation was estimated at 0/578, providing a broad measure of its impact. However, an assessment using a funnel plot and Egger's test revealed publication bias. To correct for this bias, the "trim and fill" method was applied, reducing the adjusted overall effect to 0/475. Additionally, meta-regression and subgroup analyses were performed to investigate moderating variables. The results showed that the effect of money supply on inflation differs across fixed and floating exchange rate regimes, time periods before and after 2000, and between static and dynamic models. These findings contribute to a clearer understanding of the impact of monetary and economic policies on inflation and provide a foundation for future research.

Brand community interactions and online customer support have become major platforms of brand sentiment strengthening and loyalty creation. Rapid brand responses to each customer request though inbound tweets in twitter and taking proper actions to cover the needs of customers are the key elements of positive brand sentiment creation and product or service initiative management in the realm of intense competition. In this research, there has been an attempt to collect near three million tweets of inbound customer requests and outbound brand responses of international enterprises for the purpose of brand sentiment analysis. The steps of CRISP-DM have been chosen as the reference guide for business and data understanding, data preparation, text mining, validation of results as well as the final discussion and contribution. A rich phase of text pre-processing has been conducted and various algorithms of sentiment analysis were applied for the purpose of achieving the most significant analytical conclusions over the sentiment trends. The findings have shown that the sentiment of customers toward a brand is significantly correlated with the proper response of brands to the brand community over social media as well as providing the customers with a deep feeling of reciprocal understanding of their needs in a mid-to-long range planning.

In recent years, the growth of social networks and, consequently, the increasing content of these networks have led people to use others’ opinions to make decisions for the purchase and use of products, services or even political choices. Given the fact that users' comments are textual and their reading and summarizing is timely and difficult, the automation of the extraction of opinions and sentiments of users' comments is one of the suggested solutions for online sales sites to provide more efficient services to customers for better decision making. Sentiment analysis or opinion mining is a process where people's opinions, feelings and attitudes are extracted in relation to a particular subject and are recognized as a branch of the text mining. The results of sentiment analysis can be used in recommender systems to provide more effective shopping suggestions. Information derived from the opinion mining can be used in a variety of fields such as libraries for better choices and purchases based on the users' real opinions. In this research, a system for automatically categorizing the sentiments expressed in the opinions of the buyers of the Amazon book website is presented. The system is designed using ensemble voting models to analyze the sentiment of Amazon users' comments. For all analyses, Python text mining packages are used. In ensemble method two methods are used: majority voting and weight-based voting. In the weighting method, a greater weight is assigned to a classifier by higher accuracy. By comparing the performance of the results, the weighting model is chosen as the final model for making the sentiment analysis. Results show that the proposed system can automatically classify positive and negative comments with an accuracy of over 80%.