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Market sentiment has shown to influence housing prices in the global north, but in emerging economies, the nexus is rare to chance on in the current state of science for policy direction. More importantly in the recent decade where policymakers are yet to conclude on the myriad of factors confronting the housing market in sub-Saharan Africa inhibiting affordability. This paper therefore examines the impact of market sentiment on house prices in South Africa.

The study used the Autoregressive Distributed Lag (ARDL) approach with quarterly data spanning from 2005Q1 to 2020Q4.

In all, it was established that market sentiment plays a minimal role in the property market in South Africa. But there was enough evidence of cointegration from the bound test between sentiment and house prices. Nevertheless, the lag values of sentiment pointed to a rise in house prices. Exchange rate volatilities and inflation had a statistically significant effect on prices in both the long and short term, respectively.

Policymakers could still monitor market sentiment in the housing market due to the strong chemistry between house prices and sentiment, as evidenced from the bound test, but focus on economic fundamentals as the main policy tool for house price reduction.

The findings and the creation of the sentiment index make an invaluable contribution to the paper and add to the paucity of literature on the study of market sentiment in the housing market.

The purpose of this study is to investigate the influence of firm-specific investor sentiment on Brazilian companies’ accrual-based earnings management between 2010 and 2018. The paper aims to bring deeper insight into the relationship between the investor expectations and managers’ decision-making in an emerging market.

The authors use the quantitative approach and apply a multiple linear regression model to test the relationship among the abnormal accruals, the firm-specific investor sentiment index and the control variables. The final sample includes data from 175 companies, between 2010 and 2018.

These results reveal a negative association between firm-specific investor sentiment and accrual-based earnings management, which could mean that the risk propensity of managers to manipulate earnings increases when they face known losses in the capital market.

The research findings provide a valuable understanding of how emerging capital market expectations can influence managerial decisions, such as accrual-based earnings management. The geographical area of study was limited to only Brazil.

Previous studies on developed markets show that market-wide investor sentiment positively influences accrual-based earnings management. However, the present study shows that the firm-specific investor sentiment index has a significant and negative relationship with Brazilian companies’ earnings manipulation, whereas market sentiment indicates contradictory relationship in previous studies in the country.

El propósito de este estudio es investigar la influencia del sentimiento de los inversionistas a nivel de empresa en la manipulación contable de las empresas brasileñas entre 2010 y 2018. El documento pretende aportar una visión más profunda sobre la relación entre las expectativas de los inversores y la toma de decisiones de los gestores en un mercado emergente.

usamos el enfoque cuantitativo y aplicamos un modelo de regresión lineal múltiple para probar la relación entre las acumulaciones anormales, el índice de sentimiento de los inversores a nivel de empresa y las variables de control. La muestra final incluye datos de 175 empresas, entre 2010 y 2018.

Los resultados revelan una asociación negativa entre el sentimiento de los inversores a nivel de empresa y la manipulación contable basada em acumulaciones, lo que podría significar que la propensión al riesgo de los administradores a manipular las ganancias aumenta cuando enfrentan pérdidas conocidas en el mercado de capitales.

los resultados de la investigación proporcionan una valiosa comprensión de cómo las expectativas de los mercados de capitales emergentes pueden influir en las decisiones de gestión, como la manipulación contable basada en acumulaciones. El área geográfica de estudio se limitó únicamente a Brasil y, en consecuencia, los hallazgos y conclusiones del estudio tuvieron sus límites.

estudios anteriores sobre mercados desarrollados muestran que el sentimiento de los inversores a nivel de mercado influye positivamente en la manipulación contable. Sin embargo, el presente estudio muestra que el índice de sentimiento de los inversores a nivel de empresa tiene una relación significativa y negativa con la manipulación de las ganancias de las empresas brasileñas, mientras que el sentimiento del mercado indica una relación contradictoria en estudios anteriores en el país.

Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.

After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.

The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.

The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.

This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.

This paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.

This paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.

A case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.

Proposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.

Do public opinion and political sentiments expressed on Twitter during election campaign have a meaning and message? Are they inferential, that is, can they be used to estimate the political mood prevailing among the masses? Can they also be used to reliably predict the election outcome? To answer these in the Indian context, the 2014 general election was chosen.

Tweets posted on the leading parties during the voting and crucial campaign periods were mined and manual sentiment analysis was performed on them.

A strong and positive correlation was observed between the political sentiments expressed on Twitter and election results. Further, the Time Periods during which the tweets were mined were found to have a moderating effect on this relationship.

This study showed that the month preceding the voting period was the best to predict the vote share with Twitter data – with 83.9% accuracy.

Twitter has become an important public communication tool in India, and as the study results reinstate, it is an ideal research tool to gauge public opinion.

The authors apply topic sentiment analysis (several relatively new text analysis methods) to the study of public opinion as expressed in social media by comparing reactions to the Trayvon Martin controversy in spring 2012 by commenters on the partisan news websites the Huffington Post and Daily Caller.

Topic sentiment analysis is a text analysis method that estimates the polarity of sentiments across units of text within large text corpora (Lin & He, 2009; Mei, Ling, Wondra, Su, & Zhai, 2007).

We apply topic sentiment analysis to public opinion as expressed in social media by comparing reactions to the Trayvon Martin controversy in spring 2012 by commenters on the partisan news websites the Huffington Post and Daily Caller. Based on studies that depict contemporary news media as an “outrage industry” that incentivizes media personalities to be controversial and polarizing (Berry & Sobieraj, 2014), we predict that high-profile commentators will be more polarizing than other news personalities and topics.

Results of the topic sentiment analysis support this prediction and in so doing provide partial validation of the application of topic sentiment analysis to online opinion.

We test the proposition that criminal sentiments, which we define as a negative and potent view of a juvenile delinquent (JD), moderate the effect of a delinquency adjudication on self-sentiments. We expect criminal sentiments to reduce self-evaluation and increase self-potency among juvenile delinquents but have no effect on self-sentiments among non-delinquents. We also examine the construct validity of our measure of criminal sentiments by assessing its relationship to beliefs that most people devalue, discriminate against, and fear JDs.

We test these hypotheses with self-administered survey data from two samples of college students and one sample of youths in an aftercare program for delinquent youths. We use endogenous treatment-regression models to identify and reduce the effects of endogeneity between delinquency status and self-sentiments.

Our construct validity assessment shows, as expected, that criminal sentiments are positively related to beliefs that most people devalue, discriminate against, and fear JDs. Our focal analyses support our self-evaluation predictions but not our self-potency predictions.

Our findings suggest that the negative effect of a delinquency label on JDs’ self-esteem depends on the youths’ view of the delinquency label.

This study is the first to test a modified labeling theory proposition on juvenile delinquents.

Introduction: Lending is an age-old concept, and Peer-to-Peer (P2P) lending is not new. The reduction in the issuing of loans by banks has made people switch from traditional to online mode. The introduction of the online P2P lending industry is in its nascent stage of growth. As this industry is relatively new, understanding user experience, sentiments, and emotions would be helpful for the industry to innovate as per customer requirements.

Purpose: To explore the patterns in the sentiments expressed by users of ‘Cashkumar’ based on Google reviews.

Methodology: Sentiments have been analysed using user experience in risk, cost, ease of use, and loan processing time. Python application was used for sentiment analysis of Google reviews.

Findings: The sentiment analysis results showed that the average sentiment score was 0.7144, which indicates that the user sentiment towards ‘Cashkumar’ is positive. The reviews reflect that the users, especially borrowers were satisfied with the platform’s services and happy with loan processing time. The other factors – ease of use, cost, and risk – were not given much importance by users. Both lenders and borrowers faced a few issues, but the results of the lender’s sentiment analysis could not be generalised due to a smaller number of posted reviews.

This essay studies disconnections between the macrolevel societal problems of a state and more microlevel political alignments.

Using a dataset composed of macrolevel measures of state problems and microlevel responses to a 2008 election survey, this essay applies multilevel statistical models to explain the state-to-state variance between the states on anti-abortion and pro-gun sentiments. This analysis uncovers the macro- and microlevel factors that disconnect a state’s neglect-of-children indicators from its citizens’ sentiments about abortion, and the factors that disconnect a state’s crime indicators from its citizens’ sentiments about guns.

The initial associations between a state’s indicators of neglect of children and anti-abortion sentiments are explained by the state’s lower human development (HD) and social attributes, especially religious beliefs, which predict social conservatism. The initial associations between a state’s indicators of crime and incarcerations are also explained by a state’s lower HD and the social attributes, especially religious beliefs, which predict social conservatism. Considering both abortion and guns as key indicators of social conservatism, the voters’ political choices exhibit a moralistic axiological rationality rather than a more pragmatic instrumental rationality.

The moral absolutism associated with sentiments about abortion and guns suggests that social conservatism and authoritarianism are intertwined but separate conceptions, which have similar consequences and determinants. Both may be influenced by the same changes in social and educational policies, especially the quality of education.