Changes In Sentiment Research Articles

Impact of investor sentiment on firm innovation: Evidence from textual analysis

The paper examines whether and how investor sentiment affects corporate innovation. The study finds a statistically significant relationship between investor sentiment and firm innovation. Our findings demonstrate that investor sentiment has a positive causal impact on firm innovation, as revealed through the use of the instrumental variable method and propensity score matching approach, as well as the use of changes in investor sentiment as independent variables. The results still hold after using alternative measures of investor sentiment. Our subsample analysis indicates that investor sentiment boosts innovation, regardless of R&D ratios. The estimated results after considering changes in economic conditions remain consistent with the baseline estimates. The mechanism analysis suggests that investor sentiment alleviates financing constraints and enhances R&D investment, thereby positively impacting firm innovation.

Investor Sentiment, Cross-sectional Stock Returns, and Short-Sales: Evidence From Korea

Purpose: This study investigates the return co-movements associated with investor sentiment shifts in the cross-sections under a setting where market-wide sentiment interacts with short-sale impediments.
 Design/methodology/approach: This study estimates the return sensitivity to market sentiment changes (sentiment beta) for each characteristic's portfolio by regressing the return of each quintile portfolio and various high-minus-low portfolios on the sentiment changes index. It examines whether these cross-sectionally different return co-movement patterns is more prevalent during good- than bad-sentiment periods by performing the same regression separately for the good- and bad-sentiment periods.
 Findings: The result shows that the returns of speculative stocks tend to co-move more strongly with sentiment changes than those of stable stocks, in the sense that speculative stocks have higher sentiment betas than stable stocks. The cross-sectional pattern in return co-movements becomes more pronounced during the good-sentiment period but disappears during the bad-sentiment periods.
 Research limitations/implications: This study elucidates the return co-movement behavior associated with investor sentiment changes under a setting where market-wide sentiment interacts with short-sale impediments. However, analyzing the relationship between the investment sentiment index and the short-selling activities is reserved for future research.
 Originality/value: The results provide important implications for investment strategies using investor sentiment in practice, and several suggestions for establishing investor protection policies in the highly individual-crowded market. This study will contribute to enhancing the stock market efficiency and price discovery.

A role for confidence: volition regimes and news

The economic literature presents a variety of empirical models of the structural impulse response function (SIRF) in real consumption and real output following changes in confidence or sentiment, particularly in the US and EA. This paper replicates them on the orbit of a neokeynesian dynamic stochastic general equilibrium (NK-DSGE) model characterized in particular by macroeconomic agents and derived from start to finish. Trust is specifically modeled as an endogenous variable characterized by a coalescence of three processes governed by a degree of volition, the processes being permanent technology, transient technology, and noise technology. The first two processes affect the actual production technology with a delay of one lag, while the third does not at all. The short-run responses to changes in confidence are shown whenever the degree of willingness allows confidence to change real consumption and aggregate labor, thus being non-negligible. Whenever the degree of volition was, by contrast, negligible, exogenous shocks in noise technology would cause no fluctuation in actual consumption and actual power.

Особливості соціалізуючої ролі природничого музею в умовах трансформації українського суспільства

An overview of the potential role of natural history museums in the socialisation of citizens under conditions of revolutionary worldview changes is presented, the meaning of which in Ukraine is the reinvention and final formation of the political nation and civil society. The unique set of means of information presentation and unobtrusive influence on visitors that a museum can offer for socialisation were analysed in the context of N. Lugman’s vision of society as a complex of cycles of self-reference and autopoiesis, which is realised through interpersonal and public communications. The power of the inertia of interpersonal communication is conclusive, both in the promotion of new ideas and in resistance to their influence. Therefore, reforms of society are impossible without systemic changes at this level, for which language serves as the main mediator. Language is identified as the basis for other ‘communication facilitators’: the means of storing and transmitting information and the ‘generalised exchange mediators’ that are increasingly the product of public communication. Analysing the modern changes that a museum must take into account, both in relations with the audience and in the content of messages, they are considered as a consequence of the finalisation or ‘change of sign’ in various-scale fluctuations of social and cultural dynamics, which embody fluctuations in the confrontation of certain oppositional principles. The most short-term of them in Ukraine turned out to be changes in European integration sentiments and the corresponding actions of the authorities. A fragment of the longest confrontation can be considered the transition of Humanity from the realities of state-caste societies, which are about six thousand years old, to the ideals of civil society, which is just beginning to form. For changes in the field of philosophy, the transition from the narratives of postmodernism to the foundations of metamodernism, which, in particular, includes the ‘new ontology’ of M. Hartman, has become significant. An overview of ideas and narratives that the National Museum of Natural History can share with the audience are offered. At the same time, the content of ideas and narratives is adjusted by the provisions of ontology in the interpretation of N. Hartman, and considerations regarding the possible forms of presenting information are oriented, first of all, to the understanding of the age-related features of its perception by J. Piaget.

INVESTIGATION OF CUSTOMER PREFERENCE CHANGES FOLLOWING COVID-19 MARKET DISRUPTION USING ONLINE REVIEW ANALYSIS

AbstractCOVID-19 pandemic has continued to pose a challenge to the society for almost three years, adversely affecting all segments of population in a scale unseen in the recent decades. Over the course of COVID-19 pandemic, many people have lost their jobs and income. These social and economic impacts have disrupted the market, potentially altering people's attitudes towards different product features. Therefore, this paper investigates the changes in customer preferences on various features of different products, before and after COVID-19 pandemic, using online review analysis. The proposed framework consists of four stages. Firstly, product review data is collected and preprocessed. Secondly, customer interest in product features is explored using latent Dirichlet allocation. Thirdly, customer sentiment for these features is analyzed with Valence Aware Dictionary and sEntiment Reasoner. Finally, the importance of each feature is calculated based on interpretable machine learning. The proposed method is tested on two real-world datasets – smartphone and laptop reviews. The result reveals the changes in customer sentiments and preferences for product features, thus helping companies quickly establish strategies in rapidly changing market environments.

Scapegoating Immigrants in Times of Personal and Collective Crises: Results from a Czech Panel Study

According to scapegoat theory, individuals tend to attribute personal or social problems to an out-group (real or imagined). This self-serving bias protects the ego or social identity from responsibility while increasing prejudice towards the out-group blamed for feelings of frustration. In this research note, we test this theory using five waves of the Czech Household Panel Study (CHPS 2016, 2017, 2018, 2019, 2020), which captures the tail end of the 2015–2016 refugee crisis in Europe through the lockdown in response to COVID-19 pandemic in 2020. We focus attention on both personal and professional domains, asking if factors like subjective health, work stress, relationship dissatisfaction, life dissatisfaction, and unhappiness contribute to attitudes towards immigrants over time. We also ask whether socio-political attitudes such as distrust in the government, social distrust, and political disinterest are associated with changes in anti-immigrant sentiment. Results show that personal and professional domains help explain between-individual differences in attitudes towards immigrants, while trust in the government and society are related to both within-individual change and between-individual differences in anti-immigrant sentiment.

Sentiment analysis to COVID-19 vaccination based on bert and LSTM

The novel coronavirus (COVID-19) was defined as a pandemic in March 2020, which has brought great harm to the overall economy and people's health. Efforts to suppress and end the COVID-19 have led to an unprecedented vaccine rush, stemming from extensive research by experts and authoritative institutions. Previous research has found that significant numbers of people are hesitant to get vaccinated, worrying there may be some hurt for heath. To this end, a timely understanding of people's sentiments about the COVID-19 vaccination is crucial to the popularization of vaccines. Thanks to the popularity of the mobile Internet, people have become accustomed to expressing their opinions by posting comments on the Internet, which provides a quick way to obtain data for analyzing people's sentiment changes on the COVID-19 vaccine. In this paper, to gain an intuitive understanding of people's acceptance of a particular vaccine, we propose a tweet sentiment analysis method based on Bert and LSTM. Using N-grams to evaluate the model results, an accuracy rate of 74.14% can be obtained, which verifies the effectiveness of the proposed method. Extensive experiments show that our method can provide some new insight for the later vaccination policy promotion to a certain extent.

Preheating Prosocial Behaviour

Abstract We provide new evidence on motivations for voluntary contributions to public goods in a natural setting. Using natural language processing on users’ Twitter posts, we measure revealed sentiment changes before and after making a donation to Wikipedia. We find strong evidence that sentiment improves in the hour and minutes directly before contributing, which we call ‘preheating’. Results are robust to alternative fixed effects and approaches to inference and supported by a complementary online experiment with randomised mood inducement among Twitter users. Preheating suggests that affective states influence giving in addition to reward-seeking, utility-maximising behaviour that has been documented in other contexts.

Machine Learning Approach to Analyze the Sentiment of Airline Passengers’ Tweets

As one of the most extensive social networking services, Twitter has more than 300 million active users as of 2022. Among its many functions, Twitter is now one of the go-to platforms for consumers to share their opinions about products or experiences, including flight services provided by commercial airlines. Using a machine learning approach, this study aimed to measure customer satisfaction by analyzing sentiments of tweets that mention airlines. Relevant tweets were retrieved from Twitter’s application programming interface and processed through tokenization and vectorization. After that, these processed vectors were passed into a pretrained machine learning classifier to predict the sentiments. In addition to sentiment analysis, we also performed a lexical analysis on the collected tweets to model keyword frequencies, which provided meaningful context to facilitate interpretation of the sentiments. We then applied time series methods such as Bollinger Bands to detect abnormalities in the sentiment data. Using historical records from January to July 2022, our approach was proven capable of capturing sudden and significant changes in passenger sentiments through the analysis of breakout points on the Bollinger upper and lower bounds. The methodology devised for this study has the potential to be developed into an application that could help airlines, along with other customer-facing businesses, efficiently detect abrupt changes in customer sentiments and consequently take appropriate mitigatory measures.

The Impact of Investor Sentiment on Housing Prices and the Property Stock Index Volatility in South Africa

Abstract While prior studies have examined the predictive effect of macroeconomic and country risk components on property stock index dynamics, limited explanations exist in the literature regarding the time-varying effect of investor sentiment on housing prices. Accordingly, this study assesses the impact of investor sentiment on housing properties’ returns and the effect of investor sentiment on the conditional volatility of housing price indices under different market conditions, using GARCH, GJR-GARCH, E-GARCH and Markov-switching VAR models. We found investor sentiment to significantly impact the risk premium of the property returns, where property returns increased with positive changes in investor sentiment, and conditional volatility of property returns decreased with the same changes in investor sentiment. Investor sentiment exerts positive predictive influences on the prices of small and medium houses, in both bullish and bearish market conditions but does not affect the large housing market segment. This makes the implementation of risk-related diversification across small and medium real estate portfolios more effective than large real estate portfolios. Our findings show that investor sentiment is a plausible driver of mass investor redemption actions under conditions of uncertainty.

Tweet Sentiment & Emotion Analysis

Abstract: Tweet Sentiment & Emotion Analysis using Bi-LSTM in RNN. Twitter has developed into a useful medium for sharing ideas, attitudes, and feelings. Applications like opinion mining, market research, and social trend analysis all depend on the sentiment and emotion of tweets. The Bi-LSTM architecture in RNN (Recurrent Neural Networks) is used in this study to present an advanced method for sentiment and emotion analysis on Twitter. By utilising machine learning techniques, the objective is to increase the precision and efficacy of mood and emotion analysis. The study emphasises both conventional text analysis and real-time data analysis. Organisations and governmental bodies may continuously monitor sentiment and emotional patterns on Twitter thanks to real-time analysis, which enables them to react quickly to emerging problems or crises. In a typical text analysis, historical tweet data is examined to learn more about user viewpoints, emotional patterns, and sentiment distributions. The Bi-LSTM architecture is used because it can effectively capture the context and sequential dependencies found in tweets. To ensure consistent analysis, the system gathers real-time tweets and conducts the preprocessing stages. Monitoring sentiment changes, emotional responses, and new trends on Twitter are all made possible by this study. The goal of the research is to improve the precision and efficacy of sentiment and emotion analysis on Twitter by utilising machine learning techniques, real-time data analysis, and standard text analysis. The findings and conclusions will aid in understanding public mood and feelings in the digital age.

SUSTAINABLE DEVELOPMENT GOALS, HERDING, AND RISK-AVERSE BEHAVIOUR IN MUSLIM COUNTRIES

This study examines the impact of Sustainable Development Goals (SDGs) on behavioural biases, namely herding and risk-averse behaviours, in Sharia-compliant stocks. It also explores the mediating effect of investors' sentiments on the relationship between SDGs and behavioural biases. Adopting panel data and quantile regressions, we find that that SDGs 4, 8, 10, 11, and 13 significantly and positively correlate with stock returns in Indonesia, Kuwait, Oman, and Qatar. However, SDG 7 is the only SDG goal that is significant to Saudi and UAE stock returns. The results imply a complete mediation as the SDGs have caused changes in investors' sentiment and subsequently triggered the investors to herd and become risk-averse. The impact of SDGs is more pronounced in the upper and lower quantiles of Indonesia, Saudi, and UAE stock returns, as well as the median quantile of Bahrain, Kuwait, Oman, and Qatar stock returns. The results of this study can benefit policymakers, regulators, and practitioners in identifying the best SDG practices to assist Sharia-compliant stocks in Indonesia and Gulf Cooperation Council (GCC) countries to attain better stock returns and improve investors' sentiments and behaviours. The results can also assist governments in weighing the impact and benefits of adopting SDGs in different Muslim countries.

Analysing sentiment change detection of Covid-19 tweets

The Covid-19 pandemic made a significant impact on society, including the widespread implementation of lockdowns to prevent the spread of the virus. This measure led to a decrease in face-to-face social interactions and, as an equivalent, an increase in the use of social media platforms, such as Twitter. As part of Industry 4.0, sentiment analysis can be exploited to study public attitudes toward future pandemics and sociopolitical situations in general. This work presents an analysis framework by applying a combination of natural language processing techniques and machine learning algorithms to classify the sentiment of each tweet as positive, or negative. Through extensive experimentation, we expose the ideal model for this task and, subsequently, utilize sentiment predictions to perform time series analysis over the course of the pandemic. In addition, a change point detection algorithm was applied in order to identify the turning points in public attitudes toward the pandemic, which were validated by cross-referencing the news report at that particular period of time. Finally, we study the relationship between sentiment trends on social media and, news coverage of the pandemic, providing insights into the public’s perception of the pandemic and its influence on the news.

Aggregate Investor Sentiment and Time-Varying Price Discovery: Evidence from the Options Market

Previous literature shows that the price-discovery ability of options market varies substantially over time. Using data of Shanghai Stock Exchange 50 exchange-traded fund options, this paper shows that options prices contribute relatively less to price discovery during low-sentiment periods, but the price-discovery ability of options market remains unchanged during high-sentiment periods. These results suggest that change in aggregate investor sentiment is an important source of the time variation in options’ price discovery ability. Moreover, the options market experiences greater bid-ask spreads when investor sentiment is lower, supporting a “transaction costs mechanism.” This paper fulfills related literature on the time variation in options’ price-discovery ability.

Sentiment and emotion in financial journalism: a corpus-based, cross-linguistic analysis of the effects of COVID

Sentiment and emotion play a crucial role in financial journalism, influencing market perceptions and reactions. However, the impact of the COVID-19 crisis on the language used in financial newspapers remains underexplored. The present study addresses this gap by comparing data from specialized financial newspapers in English and Spanish, focusing on the years immediately prior to the COVID-19 crisis (2018–2019) and during the pandemic itself (2020–2021). We aim to explore how the economic upheaval of the latter period was conveyed in these publications and investigate the changes in sentiment and emotion in their language compared to the previous timeframe. To this end, we compiled comparable corpora of news items from two respected financial newspapers (The Economist and Expansión), covering both the pre-COVID and pandemic periods. Our corpus-based, contrastive EN-ES analysis of lexically polarized words and emotions allows us to describe the publications’ positioning in the two periods. We further filter lexical items using the CNN Business Fear and Greed Index, as fear and greed are the opposing emotional states most often linked to financial market unpredictability and volatility. This novel analysis is expected to provide a holistic picture of how these specialist periodicals in English and Spanish have emotionally verbalized the economic havoc of the COVID-19 period compared to their previous linguistic behaviour. By doing so, our study contributes to the understanding of sentiment and emotion in financial journalism, shedding light on how crises can reshape the linguistic landscape of the industry.

Explaining US travel behavior with perceived threat of pandemic, consumer sentiment, and economic policy uncertainty

Since the COVID-19 outbreak, consumer behavior has been affected by the perceived threat of the pandemic and economic uncertainty. This paper aims to explore the dynamic effects of COVID-19, consumer sentiment, economic policy uncertainty, and fuel prices on travel behavior in the United States. Using updated daily trip data, the results show that consumer sentiment has a positive long-run impact on travel demand for air and auto, suggesting that a positive change in consumer sentiment can boost demand for these modes of transportation in the long term. Additionally, consumer sentiment has a favorable effect (1.34) on demand for long-distance trips, but it has a negative impact (−0.42) on the number of people staying at home. Economic and political shocks have a detrimental impact on demand for air and auto travel, suggesting that consumers reduce the frequency and cost of these transport services if they have pessimistic expectations about the future state of the economy and policy. However, in the short term, US travelers appear to be insensitive to shocks in consumer sentiment and economic policy uncertainty. Regarding the perceived threat of the pandemic, the results indicate that rising COVID-19 cases have a negative long-term effect on demand for air travel (−0.09) and public transit (−0.19), while they are positively associated with demand for auto travel (0.06). Similarly, the increasing number of deaths due to COVID-19 has led to a shift from shared-use mass transportation (air travel and public transit) to private autos and non-motorized travel, such as walking in the short term.

Potential Impact of the COVID-19 Pandemic on Public Perception of Water Pipes on Reddit: Observational Study.

Socializing is one of the main motivations for water pipe smoking. Restrictions on social gatherings during the COVID-19 pandemic might have influenced water pipe smokers' behaviors. As one of the most popular social media platforms, Reddit has been used to study public opinions and user experiences. In this study, we aimed to examine the influence of the COVID-19 pandemic on public perception and discussion of water pipe tobacco smoking using Reddit data. We collected Reddit posts between December 1, 2018, and June 30, 2021, from a Reddit archive (PushShift) using keywords such as "waterpipe," "hookah," and "shisha." We examined the temporal trend in Reddit posts mentioning water pipes and different locations (such as homes and lounges or bars). The temporal trend was further tested using interrupted time series analysis. Sentiment analysis was performed to study the change in sentiment of water pipe-related posts before and during the pandemic. Topic modeling using latent Dirichlet allocation (LDA) was used to examine major topics discussed in water pipe-related posts before and during the pandemic. A total of 45,765 nonpromotion water pipe-related Reddit posts were collected and used for data analysis. We found that the weekly number of Reddit posts mentioning water pipes significantly increased at the beginning of the COVID-19 pandemic (P<.001), and gradually decreased afterward (P<.001). In contrast, Reddit posts mentioning water pipes and lounges or bars showed an opposite trend. Compared to the period before the COVID-19 pandemic, the average number of Reddit posts mentioning lounges or bars was lower at the beginning of the pandemic but gradually increased afterward, while the average number of Reddit posts mentioning the word "home" remained similar during the COVID-19 pandemic (P=.29). While water pipe-related posts with a positive sentiment were dominant (12,526/21,182, 59.14% before the pandemic; 14,686/24,583, 59.74% after the pandemic), there was no change in the proportion of water pipe-related posts with different sentiments before and during the pandemic (P=.19, P=.26, and P=.65 for positive, negative, and neutral posts, respectively). Most topics related to water pipes on Reddit were similar before and during the pandemic. There were more discussions about the opening and closing of hookah lounges or bars during the pandemic. This study provides a first evaluation of the possible impact of the COVID-19 pandemic on public perceptions of and discussions about water pipes on Reddit.

Oenological potential of wines produced from disease-resistant grape cultivars.

Within the EU, changes in policy and public sentiment have made it more urgent to consider the adoption of sustainable agricultural practices. Consequently, one of the EU's goals is to reduce pesticide use by 50 per cent by 2030, including in viticulture. One of the proposed approaches is to expand the use of disease resistant hybrid grape-cultivars (DRHGC), such as 'PIWI' grapes (German, Pilzwiderstandsfähige Rebsorten), and to introduce new DRHGCs. However, the characteristics of DRHGCs are different from those of Vitis vinifera, which makes it necessary to take measures and make changes in winemaking technology to maintain high wine quality. This paper examines the chemistry of wines made from DRHGC and discusses their impact on aroma and flavor profiles. It also reviews the main winemaking practices suggested to produce high-quality wines from DRHGCs. The chemistry of DRHGCs is different to wine produced from V. vinifera, which can lead to both challenges during winemaking and unusual flavor profiles. Although newer DRHGCs have been bred to avoid unexpected flavors, many DRHGCs are still rich in proteins and polysaccharides. This can make tannin extraction difficult and produce wines with little astringency. In addition to this, new or alternative winemaking techniques such as thermovinification and the use of alternative yeast strains (non-Saccharomyces) can be used to produce wines from DRHGCs that are acceptable to consumers.

The Relationship between Investors' Sentiment and Stock Market Returns: Evidence from Margin Trading and the CSI 300 Index

In recent years, both academia and business have been more interested in investors' sentiment. Margin trading's deployment is beneficial to development of China's stock trading system. Margin trading, to some extent, reflect changes in investors' sentiment. To lessen the impact of noisy investors on stock market volatility, it is crucial to investigate the relationship between these sentiment changes and stock market returns. In this paper, the link between investors' sentiment and CSI 300 index returns is examined. To quantify investors' sentiment, the marginal trading variables eliminating macroeconomic factors are selected as proxy variables to research the correlation between investors' sentiment and CSI 300 index returns. The results show that investors' sentiment does not have a significant effect on returns. Stock market returns have a positive effect on investors' sentiment in the short run, but later this positive effect disappears quickly again. From the perspective of margin trading mechanism, this paper study the relationship between investors' sentiment and stock returns. The empirical results will provide a reference for small and medium investors to make rational decisions, and will also provide a scientific reference for improving the sentiment supervision mechanism of the stock market.

The Changing Discourses of EU Enlargement: A Longitudinal Analysis of National Parliamentary Debates

AbstractDomestic support for EU enlargement across existing member states is understood to have shifted significantly since 2004. But, how? Here, we explore how enlargement has been framed discursively in the national parliaments of eight member states. Our dataset comprises over 18,000 statements from 1989 to 2019. We link our exploration to alternative explanations for change: IR‐based shifts in how applicant states are perceived and, from comparative politics, the ‘politicisation’ of EU membership in domestic party systems. We analyse changes in salience, sentiment and thematic foci. Our results confirm shifts in the perceptions of individual applicant states over time and an increased concern with issues of ‘identity’ in relation to enlargement. We find that whilst salience has declined, sentiment has turned negative and explore the distinctive discourses of ‘challenger parties’ in this regard.