800 research outputs found

    Identifying Product Defects from User Complaints: A Probabilistic Defect Model

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    The recent surge in using social media has created a massive amount of unstructured textual complaints about products and services. However, discovering potential product defects from large amounts of unstructured text is a nontrivial task. In this paper, we develop a probabilistic defect model (PDM) that identifies the most critical product issues and corresponding product attributes, simultaneously. We facilitate domain-oriented key attributes (e.g., product model, year of production, defective components, symptoms, etc.) of a product to identify and acquire integral information of defect. We conduct comprehensive evaluations including quantitative evaluations and qualitative evaluations to ensure the quality of discovered information. Experimental results demonstrate that our proposed model outperforms existing unsupervised method (K-Means Clustering), and could find more valuable information. Our research has significant managerial implications for mangers, manufacturers, and policy makers

    Analyst Information Discovery and Interpretation Roles: A Topic Modeling Approach

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    This study examines analyst information intermediary roles using a textual analysis of analyst reports and corporate disclosures. We employ a topic modeling methodology from computational linguistic research to compare the thematic content of a large sample of analyst reports issued promptly after earnings conference calls with the content of the calls themselves. We show that analysts discuss exclusive topics beyond those from conference calls and interpret topics from conference calls. In addition, we find that investors place a greater value on new information in analyst reports when managers face greater incentives to withhold value-relevant information. Analyst interpretation is particularly valuable when the processing costs of conference call information increase. Finally, we document that investors react to analyst report content that simply confirms managers’ conference call discussions. Overall, our study shows that analysts play the information intermediary roles by discovering information beyond corporate disclosures and by clarifying and confirming corporate disclosures.http://deepblue.lib.umich.edu/bitstream/2027.42/106405/1/1229_Lehavy.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106405/4/1229_Lehavy.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106405/5/1229_Lehavy_Jul14.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106405/7/1229_Lehavy_June2015.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106405/9/1229_Lehavy_Sept2015.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106405/11/1229_Lehavy_July2016.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106405/13/1229_Lehavy_Nov2016.pdfDescription of 1229_Lehavy_July2016.pdf : July 2016 RevisionDescription of 1229_Lehavy_Sept2015.pdf : September 2015 revisionDescription of 1229_Lehavy_June2015.pdf : June 2015 RevisionDescription of 1229_Lehavy_Jul14.pdf : July 2014 revisionDescription of 1229_Lehavy.pdf : Correct original version April 7th 2014Description of 1229_Lehavy_Nov2016.pdf : November 2016 revisio

    Machine learning shows that the Covid-19 pandemic is impacting U.S. public companies unequally by changing risk structures

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    Covid-19 has impacted the U.S. economy and business organizations in multiple ways, yet its influence on company fundamentals and risk structures have not been fully elucidated. In this paper, we apply LDA, a mainstream topic model, to analyze the risk factor section from SEC filings (10-K and 10-Q), and describe risk structure change over the past two years. The results show that Covid-19 has transformed the risk structures U.S. companies face in the short run, exerting excessive stress on international interactions, operations, and supply chains. However, this shock has been waning since the second quarter of 2020. Our model shows that risk structure change (measured by topic distribution) from Covid-19 is a significant predictor of lower performance, but smaller companies tend to be stricken harder

    EXTENDING TOPIC MODELS FOR TEXT ANALYSIS OF CORPORATE RISK DISCLOSURES

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    Ph.DDOCTOR OF PHILOSOPH

    Econometrics meets sentiment : an overview of methodology and applications

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    The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

    Users’ Continued Usage of Online Healthcare Virtual Communities: An Empirical Investigation in the Context of HIV Support Communities

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    This study uses data from an online HIV/AIDS health support virtual community to examine whether users’ emotional states and the social support they receive influence their continued usage. We adopt grief theory to conceptualize the negative emotions that people living with HIV/AIDS could experience. Linguistic analysis is used to measure the emotional states of the users and the informational and emotional support that they receive. Results show that users showing a higher level of disbelief and yearning are more likely to leave the community while those with a high level of anger and depression are more likely to stay on. Users who receive more informational support are more likely to leave once they have obtained the information they sought, but those who receive more emotional support are more likely to stay on. The findings of this study can help us better understand users’ support seeking behavior in online support VCs

    Risk factor disclosures in the US Airline Industry following the COVID-19 pandemic

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    This study examines how airlines in the United States report risk at a difficult and uncertain time as a result of the COVID-19 pandemic. The fundamental differences between the years 2019 and 2020 are identified using Leximancer, which is used to locate the key ideas and themes addressed in the risk reporting sections. Following the pandemic, the themes that addressed generic and recurring hazards were afforded less weight than themes that highlighted risks particular to day-to-day business and the stock market. The findings also point to the need for corporations to disclose future-oriented risks more fully in post-COVID-19 reporting, with an emphasis on unpredictability, stock volatility, and operational disruption. This study adds to the body of knowledge on risk profiling, particularly as it relates to the airline business, and it offers stakeholders and investors a glimpse into the general concerns of airlines. The inherent information imbalance between management and investors is lessened and transparency is increased because of this improved understanding of the market.info:eu-repo/semantics/publishedVersio
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