Changes In Sentiment Research Articles

Rational catering of irrational emotions: Investor sentiment and executive tone.

In China, investors generally make decisions depending on the intonation of executive announcements. A total of 20,328 observations are sampled from the Chinese equity market between 2005 and 2019. We perform principal component analysis to produce monthly sentiment indices and calculate the weighted average of the value over the fiscal year to measure the degree of investor sentiment. The results of the empirical analysis reveal that: (1) there is a significant positive correlation between market-level investor sentiment and executive tone, (2) stock price and trading-volume pressures on executives, and firm-level investor expectation and gambling tendency positively moderate the relationship between investor sentiment and executive tone, (3) executive optimism does not mediate the association between investor sentiment and executive tone, and (4) manipulated and real intonations are non-homogeneous tactics adopted by rational managers to cater to changes in investor sentiment. These findings indicate that executives' intonations are both an extension of firms' current and past performances and managers' decision-making based on sentiments and behaviors of irrational investors, which are consistent with the Catering Theory. In addition, rational executives tend to adopt various intonation tactics to respond to investor sentiment to avoid declines in stock prices and trading volumes.

Public Opinions on COVID-19 Vaccines-A Spatiotemporal Perspective on Races and Topics Using a Bayesian-Based Method.

The COVID-19 pandemic has been sweeping across the United States of America since early 2020. The whole world was waiting for vaccination to end this pandemic. Since the approval of the first vaccine by the U.S. CDC on 9 November 2020, nearly 67.5% of the US population have been fully vaccinated by 10 July 2022. While quite successful in controlling the spreading of COVID-19, there were voices against vaccines. Therefore, this research utilizes geo-tweets and Bayesian-based method to investigate public opinions towards vaccines based on (1) the spatiotemporal changes in public engagement and public sentiment; (2) how the public engagement and sentiment react to different vaccine-related topics; (3) how various races behave differently. We connected the phenomenon observed to real-time and historical events. We found that in general the public is positive towards COVID-19 vaccines. Public sentiment positivity went up as more people were vaccinated. Public sentiment on specific topics varied in different periods. African Americans’ sentiment toward vaccines was relatively lower than other races.

Digital Events Insights

This study examines and analyzes digital events and trends from the previous year. Convert past trends into future learnings to reset objectives and strategies now. Ultimately, establish a baseline against which future changes in sentiment can be measured. This study used a mixed method. We began with pre- screening and setting objectives to develop an online survey. In addition, a few selected companies were interviewed to further investigate the research subjects’ opinions to establish benchmarks for measuring future changes. The investigation group was PCMA and the AC. After all, this study revealed six common myths that were identified during the research.

Has COVID-19 changed carer's views of health and care integration in care homes? A sentiment difference-in-difference analysis of on-line service reviews

Closer integration of health and social care is a policy priority in many countries. The COVID-19 pandemic has reinforced the necessity of joining up health and social care systems, especially in care home settings. However, the meaning and perceived importance of integration for residents’ and carers’ experience is unclear and we do not know whether it has changed during the pandemic.Using unique data from on-line care home service reviews, we combined multiple methods. We used Natural Language Processing with supervised machine learning to construct a measure of sentiment for care home residents' and their relatives’ (measured by AFINN score). Difference-in-difference analysis was used to examine whether experiencing integrated care altered these sentiments by comparing changes in sentiment in reviews related to integration (containing specific terms) to those which were not. Finally, we used network analysis on post-estimation results to assess which specific attributes stakeholders focus on most when detailing their most/least positive experiences of health and care integration in care homes, and whether these attributes changed over the pandemic.Reviews containing integration words were more positive than reviews unrelated to integration in the pre-pandemic period (about 2.3 points on the AFINN score) and remained so during the first year of the pandemic. Overall positive sentiment increased during the COVID-19 period (average by +1.1 points), mainly in reviews mentioning integration terms at the beginning of the first (+2.17, p-value 0.175) and second waves (+3.678, p-value 0.027). The role of care home staff was pivotal in both positive and negative reviews, with a shift from aspects related to care in pre-pandemic to information services during the pandemic, signalling their importance in translating integrated needs-based paradigms into policy and practice.

A sentiment analysis of Michelin-starred restaurants

PurposeWith the growing popularity of social media, it has become common practice for consumers to write online reviews to share their opinion and experience as well as consider others' reviews to inform purchase decision-making. This study investigated how online review sentiments towards four key aspects (food, service, ambience and price) change after a restaurant is awarded a Michelin Star to shed light on how the award of a Michelin Star affects online reviews as well as what factors contribute to positive online restaurant reviews.Design/methodology/approachThe authors conducted a sentiment analysis of online restaurant reviews on TripAdvisor. A total of 8,871 English-written reviews from 87 restaurants located in Europe were extracted using a web crawler developed by Beautiful Soup, and data were then processed using Semantria.FindingsThe study findings revealed that overall sentiments decreased after restaurants were awarded a Michelin Star, in which service sentiment was the most affected aspect, followed by food and ambience. Yet, price sentiment showed a prominent increase. This provides valuable insights for Michelin-starred restaurant operators and owners to create a unique and compelling gastronomic experience that triggers positive online reviews.Practical implicationsThe results of this study argue that consumers tend to hold higher expectations for this type of upscale restaurants given its recognition and quality assurance, so they are more likely to have negative feelings when their expectations are disconfirmed. Therefore, restaurants should continuously improve their food and service while paying attention to small details such as ambience, through creativity and innovation. Also, high-end restaurants, especially Michelin-starred restaurants, usually have the edge in premium pricing, yet competitive pricing may backfire considering its perceived luxurious values.Originality/valueThis study analyzed changes in customer sentiments when a restaurant is awarded a Michelin Star through text analytics. Through the lens of online restaurant reviews, the study findings contribute to identifying aspects that are most or least affected by the award of a Michelin Star as well as highlight the role of ambience in customer satisfaction which might have been overlooked in previous studies.

Analysing online travel reviews to identify temporal changes of a destination image

The goal of this study is to examine how destination image and destination sentiment change over time using content from online travel reviews. We explore the evolution of Finland’s destination image from 2013 to 2018 by analysing 10,197 unstructured Chinese travel reviews. With a mixed analysis approach, we identify the evolutions of the perception of destination attributes, destination structure, and destination sentiment at functional, the mixed functional-psychological, and the psychological levels. The results show that the perceptions of destination attributes can evolve and change surprisingly rapidly, even in the case of a country-level destination. Although the structure of destination image seems to be stable, individual dimensions may evolve rapidly. In terms of destination sentiment, the evolving trends of destination sentiment can be even in the opposite direction of the evolution of destination attributes. These findings improve the research on the dynamic characteristics of destination image and provide industry insights for destination marketers in formulating long-term marketing strategies and monitoring destination image development.

Social Media Users' Perceptions of a Wearable Mixed Reality Headset During the COVID-19 Pandemic: Aspect-Based Sentiment Analysis.

BackgroundMixed reality (MR) devices provide real-time environments for physical-digital interactions across many domains. Owing to the unprecedented COVID-19 pandemic, MR technologies have supported many new use cases in the health care industry, enabling social distancing practices to minimize the risk of contact and transmission. Despite their novelty and increasing popularity, public evaluations are sparse and often rely on social interactions among users, developers, researchers, and potential buyers.ObjectiveThe purpose of this study is to use aspect-based sentiment analysis to explore changes in sentiment during the onset of the COVID-19 pandemic as new use cases emerged in the health care industry; to characterize net insights for MR developers, researchers, and users; and to analyze the features of HoloLens 2 (Microsoft Corporation) that are helpful for certain fields and purposes.MethodsTo investigate the user sentiment, we collected 8492 tweets on a wearable MR headset, HoloLens 2, during the initial 10 months since its release in late 2019, coinciding with the onset of the pandemic. Human annotators rated the individual tweets as positive, negative, neutral, or inconclusive. Furthermore, by hiring an interannotator to ensure agreements between the annotators, we used various word vector representations to measure the impact of specific words on sentiment ratings. Following the sentiment classification for each tweet, we trained a model for sentiment analysis via supervised learning.ResultsThe results of our sentiment analysis showed that the bag-of-words tokenizing method using a random forest supervised learning approach produced the highest accuracy of the test set at 81.29%. Furthermore, the results showed an apparent change in sentiment during the COVID-19 pandemic period. During the onset of the pandemic, consumer goods were severely affected, which aligns with a drop in both positive and negative sentiment. Following this, there is a sudden spike in positive sentiment, hypothesized to be caused by the new use cases of the device in health care education and training. This pandemic also aligns with drastic changes in the increased number of practical insights for MR developers, researchers, and users and positive net sentiments toward the HoloLens 2 characteristics.ConclusionsOur approach suggests a simple yet effective way to survey public opinion about new hardware devices quickly. The findings of this study contribute to a holistic understanding of public perception and acceptance of MR technologies during the COVID-19 pandemic and highlight several new implementations of HoloLens 2 in health care. We hope that these findings will inspire new use cases and technological features.

Urban greenspace helps ameliorate people's negative sentiments during the COVID-19 pandemic: The case of Beijing.

The COVID-19 pandemic has had negative effects on people's mental health worldwide, especially for those who live in large cities. Studies have reported that urban greenspace may help lessen these adverse effects, but more research that explicitly considers urban landscape pattern is needed to understand the underlying processes. Thus, this study was designed to examine whether the resident sentiments in Beijing, China changed before and during the pandemic, and to investigate what urban landscape attributes – particularly greenspace – might contribute to the sentiment changes. We conducted sentiment analysis based on 25,357 geo-tagged microblogs posted by residents in 51 neighborhoods. We then compared the resident sentiments in 2019 (before the COVID-19) with those in 2020 (during the COVID-19) using independent sample t-tests, and examined the relationship between resident sentiments and urban greenspace during the COVID-19 pandemic phases using stepwise regression. We found that residents' sentiments deteriorated significantly from 2019 to 2020 in general, and that urban sentiments during the pandemic peak times showed an urban-suburban trend that was determined either by building density or available greenspace. Although our analysis included several other environmental and socioeconomic factors, none of them showed up as a significant factor. Our study suggests the effects of urban greenspace and building density on residents' sentiments increased during the COVID-19 pandemic and that not all green spaces are equal. Increasing greenspace, especially within and near neighborhoods, seems critically important to helping urban residents to cope with public health emergencies such as global pandemics.

A robust textual analysis of the dynamics of Hong Kong property market

AbstractMarket sentiments influence the dynamics of Hong Kong’s macro‐critical property market, but the unobservable nature of market sentiments makes it difficult to systemically assess this sentiment channel. Using text mining techniques, this paper sets up a news‐based property market sentiment index and a Google Trends‐based buyer incentive index for Hong Kong and studies the sentiment channel of transmission in the Hong Kong property market. The news‐based property market sentiment index can reflect the change in sentiments in past key events, with the sentiments in the primary market tending to lead that of the secondary market during the low housing supply period. For the Google Buyer Incentive Index, we find that it has value‐added in forecasting (or nowcasting) the official property price index. In mapping out the sentiment channel using a structural vector‐autoregressive model, we find that an improvement in market sentiments could stimulate buyers’ incentives, which then together would affect property prices and transaction volumes.

Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+BiLSTM.

With the development of the Internet, more and more people prefer to confide their sentiments in the virtual world, especially those with depression. The social media where people with depression collectively leave messages is called the “Tree Hole”. The purpose of this article is to support the “Tree Hole” rescue volunteers to help patients with depression, especially after the outbreak of COVID-19 and other major events, to guide the crisis intervention of patients with depression. Based on the message data of “Tree Hole” named “Zou Fan”, this paper used a deep learning model and sentiment scoring algorithm to analyze the fluctuation characteristics sentiment of user’s message in different time dimensions. Through detailed investigation of the research results, we found that the number of “Tree Hole” messages in multiple time dimensions is positively correlated to emotion. The longer the “Tree Hole” is formed, the more negative the emotion is, and the outbreak of COVID-19 and other major events have obvious effects on the emotion of the messages. In order to improve the efficiency of “Tree Hole” rescue, volunteers should focus on the long-formed “Tree Hole” and the user groups that are active in the early morning. This research is of great significance for the emotional guidance of online mental health patients, especially the crisis intervention for depression patients after the outbreak of COVID-19 and other major events.

3rd International Conference on Business, Economics and Finance (ICBEF) Proceedings

NEW BUSINESS PARADIGM DURING ECONOMIC UNCERTAINTIES After two successful conferences (2017 and 2019) UBDSBE hosted the 3rd International Conference on Business, Economics and Finance (3rd ICBEF) from 1st to 3rd November 2021. This year’s conference was held virtually in view of the current COVID-19 pandemic. The theme of the conference was ‘New Business Paradigm During Economic Uncertainties’. The main objective of the 3rd ICBEF was to provide a forum for all researchers and practitioners to discuss the challenges faced during economic uncertainties, share experiences, and suggest possible solutions. Business cycles are a known phenomenon of free market economies where businesses experience booms and bust. However, the causes of these business cycles are attributed to a number of factors including rapid pace of innovations, changes in consumer and investor sentiments, changes in regulatory environment and natural disasters, such as the recent COVID-19 pandemic. Some observers consider economic crisis as a period of ‘creative destruction’, which brings new ideas, technologies, products and services that could lead to economic growth. Economic uncertainties that disrupt the existing way of doing businesses motivate governments and businesses to explore opportunities to reconstruct conditions for profitable and sustainable business through redesigning institutional and cultural framework within which firms operate. The recent uncertainties created by COVID-19 pandemic leave a much deeper impact on businesses, especially SMEs.

Corporate governance, market conditions and investors’ reaction to information signals

We examine and compare the extent to which the reaction of investors to earnings announcements is influenced by a firm’s governance profile and prevailing market conditions. We find that firms with better governance characteristics experience a larger initial reaction to both good and bad earnings announcements regardless of the prevailing sentiment and uncertainty conditions. However, the influence of governance is constrained to the announcement period. We demonstrate that changes in market uncertainty and/or investor sentiment are related to the post-earnings announcement drift. We also find that a major channel through which greater corporate governance influences the market response to unexpected earnings news is by lowering information uncertainty and so providing greater clarity of the implication of the news for firm value. Finally, we establish that two types of uncertainties (market and information) have very different influence on investor’s response to information signals. JEL Classification: D81, G10, G14, G30, G32

The Impact of Two Kinds of Responses to COVID-19 on the Stock Prices of Multinational and Local Enterprises

<p>Ever since COVID-19 became popular around the world, there are two kinds of measures taken by governments to respond to the impact on stock markets, including controlling the number of new cases and promulgating economic policies to stimulus economy. This paper studies how the two measures, controlling the spread of COVID-19 and formulating economic policies, impact stock returns in United States and what are the differences. Additionally, this paper analyses if these two measures have the same impact on multinational companies and local companies. By using CAPM and descriptive statistical analysis, the researchers find that economic stimulus is effective in short term but controlling the spread of pandemic plays the key part in long-term economic growth, international trades make a difference in reducing risk during COVID-19. Finally, to understand is the impact of investor sentiment on stock prices the reason for these two responses to changes in stock returns, Fama-French three factor with sentiment factor added is used to explore the relationship between investor sentiment and stock returns. The conclusion is that changes in investor sentiment are positively correlated with the excess return of the portfolio.</p>

Practical Applications of Analysis and Comparison of Natural Language Processing Algorithms as Applied to Bitcoin Conversations on Social Media

In <b><i>Analysis and Comparison of Natural Language Processing Algorithms as Applied to Bitcoin Conversations on Social Media</i></b>, from the February 2022 issue of <b><i>The Journal of Investing</i></b>, authors <b>Benjamin McMillan</b>, <b>Joshua Myers</b> (both of <b>IDX Digital Assets</b>), <b>An Nguyen</b> (of <b>Johns Hopkins University</b>), <b>Don Robinson</b>, and <b>Mark Kennard</b> (both of <b>Palladiem</b>) studied how natural language processing (NLP) algorithms capture investor sentiment online and whether changes in sentiment can predict investment returns. The authors used four different NLP algorithms to measure trends in sentiment toward bitcoin on two Reddit message boards, r/bitcoin (which focuses on bitcoin) and r/investing (which covers general investments). The authors used sentiment analysis to determine whether positive or negative posts about bitcoin correlated with bitcoin’s short-term performance. The authors found a statistically significant correlation between negative posts on r/bitcoin and bitcoin price changes. Bitcoin’s performance tended to drop in the five days before a negative post and rise in the five days afterward. There was no such correlation for positive posts on r/bitcoin or for any posts on r/investing. Therefore, clusters of negative posts on r/bitcoin may mark a local bottom and be predictive of increased performance.

Consumer shopping emotion and interest database: a unique database with a multimodal emotion recognition method for retail service robots to infer consumer shopping intentions better than humans

Empowering retail service robots with empathy is one of the current research hotspots in the field of artificial intelligence. Identifying consumer emotions, understanding the changes in shopping interests, and developing appropriate sales strategies is a challenging task for retail service robots. We investigate the feasibility of using computer vision methods for empowering robots with empathy by examining the correlation between consumer emotion and levels of shopping interest. To this end, we construct the first video database of consumer sentiment changes in a business context and propose a deep learning method that uses multimodal information to infer consumers’ shopping intentions, and conduct preliminary experimental validation on this database. The experimental results show that the proposed method is 7% and 10% more accurate than manual assessment (n = 40) in identifying consumer emotions and predicting consumer shopping interest levels, respectively. Thus, the proposed method is valid and effective. We anticipate that the results of this study will have considerable implications for human–computer interaction research in service robots.

Monitoring the Emotional Response to the COVID-19 Pandemic Using Sentiment Analysis: A Case Study in Mexico.

The world is facing the COVID-19 pandemic, leading to an unprecedented change in the lifestyle routines of millions. Beyond the general physical health, financial, and social repercussions of the pandemic, the adopted mitigation measures also present significant challenges in the population's mental health and health programs. It is complex for public organizations to measure the population's mental health in order to incorporate it into their own decision-making process. Traditional survey methods are time-consuming, expensive, and fail to provide the continuous information needed to respond to the rapidly evolving effects of governmental policies on the population's mental health. A significant portion of the population has turned to social media to express the details of their daily life, rendering this public data a rich field for understanding emotional and mental well-being. This study aims to track and measure the sentiment changes of the Mexican population in response to the COVID-19 pandemic. To this end, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. Sentiment analysis polarity scores, which oscillate around -0.15, show a weekly seasonality according to Twitter's usage and a consistently negative outlook from the population. It also remarks on the increased controversy after the governmental decision to terminate the lockdown and the celebrated holidays, which encouraged the people to incur social gatherings. These findings expose the adverse emotional effects of the ongoing pandemic while showing an increase in social media usage rates of 2.38 times, which users employ as a coping mechanism to mitigate the feelings of isolation related to long-term social distancing. The findings have important implications in the mental health infrastructure for ongoing mitigation efforts and feedback on the perception of policies and other measures. The overall trend of the sentiment polarity is 0.0001110643.

Persistence of investor sentiment and market mispricing

AbstractWe investigate changes in US market sentiment using structural break analysis over a period of five decades. We show that investor sentiment was trending and nonstationary from 1965 to 2001, a period associated with numerous crashes. Since 2001, sentiment has been substantially more mean reverting, implying the diminished effect of noise investors and their associated mispricing. We illustrate how these changes in sentiment persistence affect equity anomalies and assess the predictive power of sentiment on short‐run returns when regime changes are considered. Our findings suggest that the presence of sentiment‐driven investors and their market impact is significantly time‐variant.

소비 심리 변화가 보험 해약에 미치는 영향

이 연구는 보험 해약이 보험소비자의 심리와 그에 따른 실제 행동에 기반을 둔다는 데 초점을 맞춰 보험 해약의 결정요인을 실증 분석하였다. 특히 소득, 금리, 실업률 등 긴급자금가설, 이자율가설에 기초한 거시경제지표 외에 소비자심리지수를 소비 심리 대용변수로 하여 소비자 심리 변화가 보험 해약에 미치는 영향에 대해 중점적으로 살펴보았다. 분석방법으로는 생명보험과 장기손해보험상품을 대상으로 시계열 다중회귀모형을 적용하였다. 분석 결과, 소비자심리지수(CCSI)와 생명보험 및 장기손해보험 해약률 사이에는 분석 대상 전 시차에서 통계적으로 유의미한 관계가 관찰되었으며, 특히 5개월 시차에서 가장 설명력 높은 모형이 도출되었다. 이러한 결과는 보험회사가 계약자의 해약 행동을 예측하여 해지 리스크를 측정하고 해지 방어 전략을 수립하는데 있어 소비자심리지수(CCSI)를 유용하게 활용할 수 있음을 시사한다.

A Framework for Topic Evolution and Tracking Their Sentiments With Time

With the ongoing covid-19 pandemic, people rely on online communication to remain connected as a precautionary measure to maintain social distancing. When we have no one on our side to listen and console us in state of fear and dilemma, we try to find comfort in anonymity of social media. Tracking real-time changes in sentiments are quite difficult as it could not correlate well with human understanding and emotions, which changes with time and many other factors. Collecting sentiments from users on search results, news articles, paintings, photographs are nowadays common. This is a more robust and effective method as traditional ways do not rely on a lot of retrospectives. In this paper, we will be analyzing the data collected from Twitter on Covid-19 and see topic modelling can be meant to detect sentiment analysis. The challenge is here we need to see results over time, and changes detect in topics and sentiments. We analyze our method over covid-19 data and farmer’s protest. Results from this experiment using the proposed methodology are promising and giving valuable insights.

Agricultural Economic Risk Forecast Based on Data Mining Technology.

In order to improve the effect of agricultural economic risk forecast, this paper studies the agricultural economic risk forecast combined with data mining technology and builds an intelligent agricultural economic risk forecast system. Moreover, this paper employs a dynamic factor model to estimate common factors that drive changes in target topics. In order to construct a sentiment index that can reflect the overall operating situation of the macroeconomy, this paper improves the agricultural economic risk mining algorithm and standardizes the sentiment value corresponding to the target theme. In addition, this article analyzes the sentiment changes of its individual topics one by one in combination with the specific economic environment. The simulation study shows that the agricultural economic risk forecast system based on data mining technology proposed in this paper has a good effect.