Content analysis of business communication: introducing a German dictionary

Bannier, Christina E.; Pauls, Thomas

doi:10.1007/s11573-018-0914-8

Cited by 27 publications

(15 citation statements)

References 69 publications

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“…-First, the word list is prepared by Bannier et al (2018). This is the German equivalent of the English original dictionary provided by Loughran and McDonald (2016).…”

Section: Dictionary-based Methodsmentioning

confidence: 99%

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German forecasters’ narratives: How informative are German business cycle forecast reports?

Müller

2021

Empir Econ

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Based on German business cycle forecast reports covering 10 German institutions for the period 1993–2017, the paper analyses the information content of German forecasters’ narratives for German business cycle forecasts. The paper applies textual analysis to convert qualitative text data into quantitative sentiment indices. First, a sentiment analysis utilizes dictionary methods and text regression methods, using recursive estimation. Next, the paper analyses the different characteristics of sentiments. In a third step, sentiment indices are used to test the efficiency of numerical forecasts. Using 12-month-ahead fixed horizon forecasts, fixed-effects panel regression results suggest some informational content of sentiment indices for growth and inflation forecasts. Finally, a forecasting exercise analyses the predictive power of sentiment indices for GDP growth and inflation. The results suggest weak evidence, at best, for in-sample and out-of-sample predictive power of the sentiment indices.

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“…-First, the word list is prepared by Bannier et al (2018). This is the German equivalent of the English original dictionary provided by Loughran and McDonald (2016).…”

Section: Dictionary-based Methodsmentioning

confidence: 99%

“…The last-mentioned word list is well established for textual analysis in finance-and accounting-specific contexts. The word list prepared by Bannier et al (2018) includes over 2200 positive and 10,000 negative word forms. The dictionary is binary coded for polarity in positive and negative terms.…”

Section: Dictionary-based Methodsmentioning

confidence: 99%

German forecasters’ narratives: How informative are German business cycle forecast reports?

Müller

2021

Empir Econ

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“…Also, the list must be in the same language as the analysed text. A simple translation of a list of specific content into English could lead to an erroneous sentiment analysis result [2] . Finally, these investor sentiment measures currently show overall market sentiment rather than asset-specific sentiment [15] .…”

Section: Sentiment Analysis Backgroundmentioning

confidence: 99%

Sentiment analysis based on a social media customised dictionary

et al. 2021

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This article presents a methodology to classify the polarity of words from selected Tweets. Usually, social media sentiment (SMS) is lexically determined, manually or by machine learning. However, these methods are either slow or based on a pre-established dictionary, thus not providing a customised analysis. We propose a methodology that, after having mined the topic-related Tweets, filters relevant words based on the mean and standard deviation frequency in positive and negative market days to remove neutral terms. Subsequently, through an ad hoc perceptual mapping, we assign a polarity to the dataset. This method allows the building of a dictionary associated with the investor sentiment customised to that organisation. A practical application was carried out to test the proposed methodology. The results were significant and in line with the behavioural finance theory, confirming that irrational investor feelings—expressed via social media—drive a portion of asset prices. Results also confirm the investor asymmetric behaviour under gain or loss scenarios, with the latter generating more impact than the former because people are risk-averse. The proposed method is expected to identify patterns of behaviour in social media linked to market oscillations, thereby contributing to risk management and optimising decision-making in the stock market. The use of both statistical and perceptual map filters allows a specific asset dictionary to be built; Textual sentiment analysis based on social media; The proposed method efficiently overcomes generic dictionaries and language issues.

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“…Translation invariably comes with additional problems to solve. Bannier et al (2019) start from the English Loughran and McDonald lexicon by doing word-by-word translation to German. On top of that, they deal with distinct grammatical features of the German language related to inflectional and lexical morphology, as well as compound wording.…”

Section: Lexicon-based Approachesmentioning

confidence: 99%

Econometrics Meets Sentiment: An Overview of Methodology and Applications

Algaba

Ardia

Bluteau³

et al. 2020

Journal of Economic Surveys

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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.

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Content analysis of business communication: introducing a German dictionary

Cited by 27 publications

References 69 publications

German forecasters’ narratives: How informative are German business cycle forecast reports?

German forecasters’ narratives: How informative are German business cycle forecast reports?

Sentiment analysis based on a social media customised dictionary

Econometrics Meets Sentiment: An Overview of Methodology and Applications

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