- 3. Apply lexicon-based sentiment analysis using SentiWS (Sentiment WortSchatz), which is a publicly available German-language resource provided by Remus et al. (2010). This provides a list of positive and negative sentiment-bearing words that are weighted within the interval of [−1, 1], which is used to evaluate the average tone of an article. 4. Consistency check: Manual screening of all relevant articles and categorize between (i) negative, (ii) neutral, and (iii) positive. Compare my evaluation with others and with the results produced with SentiWS.23 5. Calculate the yearly average of all Telekom shares articles that are available within a year. Afterwards, compute the growth rate of the yearly sentiment measures.
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- A Sentiment analysis In this appendix, I present how I conduct a sentiment analysis using newspaper articles from the Handelsblatt. The data is provided by the Forecasting and Economic Policy Department of DIW Berlin. All articles are converted in XML-format. For my purpose, I utilize articles from 1998 through 2002. 1. Import all articles (XML-Document) using Python. The XML format includes tags that indicate the topic of a certain article. Use category “Firms” and extract all articles that report news about Deutsche Telekom within this category. This includes all sort of information of the company, starting from sport sponsorship to IPOs. Afterwards, manually filter out articles that exclusively include information on Telekom shares. See Table 7. 2. Tokenize and lemmatize all texts using Natural Language Toolkits in Python (SpaCY).
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