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This paper investigates the role of sentiment and specific emotion analysis in forecasting donation behaviour within the context of social networking services (SNSs). The study empirically examines the influence of sentiment and specific emotion analysis on donation behaviour for two non-profit organizations (NPOs): The Fred Hollows Foundation (The Foundation) in both Australia and New Zealand, and The University of Auckland (UOA) in New Zealand. We collected and analysed 298,569 tweets from 106,349 users mentioning these NPOs, along with 5,175,359 tweets mentioning the top 20 US brands from 1,623,113 users. We found that NPOs are often associated with brands that induce joy. Furthermore, sadness expressed by marketers and joy expressed by users positively affected donations to The Foundation, while user-expressed anger positively influenced donations to UOA within the same month. A two-month rolling average analysis highlighted the significant effect of lingering negative emotions on monthly donations over time. Specific emotion analysis outperforms sentiment analysis by demonstrating a higher effect size (R 2 ). We advocate for the application of the transformer-transfer learning method for specific emotion analysis when scrutinizing large-scale social media data and devising fundraising strategies. [ABSTRACT FROM AUTHOR]

Capitalizing on massive sentiment diffusion and mobilization aided by mass media and social media nowadays, we introduce a general theory of host country sentiments to illuminate the granular, context-situated, and time dynamic power of social sentiments. We posit that host country is a value-laden context wherein a foreign entity's strategic action stimulates host country stakeholders' social sentiments and engagement in systems of discourses ("sentiment arousal"), mobilizes social sentiments toward the foreign entity ("sentiment competence"), and legitimizes social sentiments and the strategic action of the foreign entity ("sentiment resonance"). To test our theory, we employ a context of inward acquisitions by emerging market firms in an advanced economy, and postulate that host country sentiments toward objects of the inward acquisition (deal, acquirer, and inward investments from home country) will influence the likelihood of deal completion, an indication of host country legitimacy. We further highlight the patterns of host country sentiment dynamics, and showcase how sentiment volatility, wavelength, and augmentation exert heterogeneous effects on deal completion. Overall, we advocate a theory of host country sentiments in international management research that elucidates the interplay among host country sentiments, systems of social discourses, and multinational enterprise strategic outcomes in a host country. [ABSTRACT FROM AUTHOR]

Although there is previous research on textual analysis in accounting, little is understood about the effects of the tone and sentiments of the text disclosed in annual report filings (10-K) on the investor assessment of the future performance of the firm. This study inks the tone and sentiments of the text disclosed in the corporate annual reports and expectations regarding the future performance of the firm. We focus on two specific sections of 10-K filings the risk factor disclosures (Item 1A) and the management's discussion (Item 7) filling a gap in the accounting literature. There are two competing predictions regarding how the tone and sentiments of the text could impact the investor assessment about the future performance of the firm. First, the tone and sentiments of the text in Items 1A and 7 may have an effect on the investor assessment of the future performance of the firm. Second, only the tone and sentiments of Item 7 may have an effect on the investor assessment, given the fact that Item 1A only discusses the general risk factors that are present and not necessarily any specific risk factors to the firm. We find that only the tone and sentiments of the text in Item 7 has an effect on the investor assessment of the firm. This result helps explain the channels through which the tone and sentiments of the text impacts the future performance. [ABSTRACT FROM AUTHOR]

Textual data require an analytical trade-off between breadth and depth. Automated approaches locate patterns across large swaths of data points but sacrifice qualitative insight because they are not well equipped to deal with context-determined ways to express meaning, like figurative language. To strengthen the power of automated text analysis, researchers seek hybrid methodologies that combine computer-augmented analysis with sociocultural researcher insights based on qualitative textual interpretation. This article demonstrates a new method, which the authors term metaphor-enabled marketplace sentiment analysis (MEMSA). Building on existing automated text analysis methodologies linking word lists to sentiments, MEMSA adds metaphors that associate topics with sentiments across domains. Using MEMSA, researchers can leverage the sentiment potential of these located metaphors and scale insights to the level of big textual data by employing a dictionary approach enhanced by a specific and useful linguistic property of metaphors: their predictable structure in text (something is something else). This article shows that metaphors add associative detail to sentiments, revealing the targets and sources of sentiments that underlie the associations. Understanding nuanced market sentiments enables marketers to identify sentiment-based trends embedded in market discourse, so they can better formulate, target, position, and communicate value propositions for products and services. [ABSTRACT FROM AUTHOR]

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Analyzing 48 foreign exchange (FX) rates and 1.2 million FX-related news articles over a 35-year period, using digital textual analysis, we find that a currency reversal investment strategy that buys (sells) currencies with low (high) media sentiment offers strong positive and statistically significant returns and Sharpe ratios. The results are robust and the strategy adds value over other currency premia determinants. Analysts' forecasts systematically mispredict the reversal strategy. This is the first article to show that price reversals based on media sentiment are a well-defined feature of the FX market. [ABSTRACT FROM AUTHOR]

Brand-related content cocreated by consumers can play a crucial role in brand–consumer interactions and provide brands with valuable insights hidden in vast seas of unstructured data. We propose and evaluate a framework integrating a social network approach and scalable automated content analysis of texts and visuals for studying brand-related communication on social media. To illustrate the proposed approach, we use Twitter content related to two brands: Barclays and Sierra Club. By applying network clustering algorithms we identify different types of organically emerging communities around brands. Cluster-specific diffusion leaders are identified using their in-degree centrality values. To examine the unique characteristics of brand-related content within each cluster, we apply and assess the accuracy of popular off-the-shelf solutions for text and image analysis, also known as application programming interfaces (APIs). Of six sentiment analysis solutions, only one shows acceptable reliability levels. For computer vision APIs, we first identify labels that have unclear or imprecise meaning and calculate accuracy levels, resulting in acceptable accuracy levels for four of the five APIs. We discuss conceptual and practical implications of this integrative approach and of the technological hurdles that these popular automated content analysis applications pose. [ABSTRACT FROM AUTHOR]

The sport industry is at an inflection point where all major professional sport leagues in North America are struggling to attract the same avid fans, once a staple within the industry. Furthermore, not only are the number of avid fans decreasing, the average age of avid sport fans is increasing. In other words, the current state of the sport industry indicates that fans are decreasing in number while simultaneously aging out of traditional professional sports. The current study aimed to investigate the ability of a traditional sport, NASCAR (National Association for Stock Car Auto Racing) in this case, to utilize an esport, iRacing, to attract new and more diverse fans. Through a partnership with Zoomph, Twitter content was analyzed and revealed the shift from traditional racing to iRacing by NASCAR. Due to the COVID-19 pandemic, it assisted them in attracting new, more diverse fans whose sentiment on Twitter was positive. Overall, the current study demonstrated that traditional sport properties may be well served by tapping into the esport space in an authentic way to enhance their core product(s). [ABSTRACT FROM AUTHOR]

The authors examine the effects of a firm's and its competitors' online reviews on its demand within the hotel industry. The authors leverage a unique data set of actual bookings from properties of a major hotel chain in six different markets in the United States, supplemented with online reviews garnered from a popular social media platform. The findings indicate that not only a hotel's own reviews but also its competitors' reviews have a significant impact on the hotel's booking performance. The impact of review sentiment is amplified if the focal hotel also charges higher prices or when the volume of reviews is high. The authors establish heterogeneous effects across consumer segments (business vs. leisure travelers) and by the type of review content (objective vs. subjective attributes to assess quality). Specifically, both a hotel's own reviews and its competitors' reviews have a larger impact on bookings for business travelers compared with leisure travelers, and for reviews that mainly discuss subjective attributes, for which consumers need to rely on the experiences of others to assess the quality of a hotel prior to their stay. The study provides a set of comprehensive insights on the impact of both own and competitors' online reviews on a focal hotel's bookings. [ABSTRACT FROM AUTHOR]

Sentiment analysis has fundamentally changed marketers' ability to assess consumer opinion. Indeed, the measurement of attitudes via natural language has influenced how marketing is practiced on a day-to-day basis. Yet recent findings suggest that sentiment analysis's current emphasis on measuring valence (i.e., positivity or negativity) can produce incomplete, inaccurate, and even misleading insights. Conceptually, the current work challenges sentiment analysis to move beyond valence. The authors identify the certainty or confidence of consumers' sentiment as a particularly potent facet to assess. Empirically, they develop a new computational measure of certainty in language—the Certainty Lexicon—and validate its use with sentiment analysis. To construct and validate this measure, the authors use text from 11.6 million people who generated billions of words, millions of online reviews, and hundreds of thousands of entries in an online prediction market. Across social media data sets, in-lab experiments, and online reviews, the authors find that the Certainty Lexicon is more comprehensive, generalizable, and accurate in its measurement compared with other tools. The authors also demonstrate the value of measuring sentiment certainty for marketers: certainty predicted the real-world success of commercials where traditional sentiment analysis did not. The Certainty Lexicon is available at www.CertaintyLexicon.com. [ABSTRACT FROM AUTHOR]

Big data and innovative research methods are two rapidly evolving trends that are transforming how we conduct research in sport management. Considering the natural relationship between social media, which is widely recognized as a major big-data source, and sport, this commentary centers on contemporary research method applied to social media data. In doing so, it discusses contemporary innovative techniques for social media data, focusing on exploring ways to access social media data, the natural language-processing techniques used, the challenges they address, the strengths and limitations of different techniques, and the ethical and privacy considerations associated with their use. Furthermore, the commentary demonstrates that using sentiment-analysis tools (e.g., Syuzhet, Bing, and AFFIN) is appropriate and efficient in analyzing sport's social media data. Thus, a rigorous application of contemporary innovative techniques can significantly shape the future of sport management research. However, researchers must exercise caution when considering the source and preprocessing of the data prior to applying advanced analytical techniques. [ABSTRACT FROM AUTHOR]

Growing AI readership (proxied for by machine downloads and ownership by AI-equipped investors) motivates firms to prepare filings friendlier to machine processing and to mitigate linguistic tones that are unfavorably perceived by algorithms. Loughran and McDonald (2011) and BERT available since 2018 serve as event studies supporting attribution of the decrease in the measured negative sentiment to increased machine readership. This relationship is stronger among firms with higher benefits to (e.g., external financing needs) or lower cost (e.g., litigation risk) of sentiment management. This is the first study exploring the feedback effect on corporate disclosure in response to technology. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]