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Ee ———————E————— EE Note: Heatmap for documents published in 2010 (tf-idf weighted dtm). The color key corresponds to probabilities for each topic being discussed during the corresponding interest rate decision meeting.

Figure 8 Ee ———————E————— EE Note: Heatmap for documents published in 2010 (tf-idf weighted dtm). The color key corresponds to probabilities for each topic being discussed during the corresponding interest rate decision meeting.

Related Figures (20)

Note: This table presents an excerpt of a document term matrix (dtm). Table 1
Note: This table presents an excerpt of a document term matrix (dtm). Table 1
Fig. 1. Histogram - tidytext. Note: Histogram containing most popular terms within the tidytext table. Notes: This table presents an excerpt of a dtm with tf-idf weighting methodology. The highest values for the selected sample are highlighted in gray.
Fig. 1. Histogram - tidytext. Note: Histogram containing most popular terms within the tidytext table. Notes: This table presents an excerpt of a dtm with tf-idf weighting methodology. The highest values for the selected sample are highlighted in gray.
Note: Terms most correlated with the term “bond”. Table 4
Note: Terms most correlated with the term “bond”. Table 4
Fig. 2. Histogram - Corpus. Note: Histogram containing most popular terms within the corpus.
Fig. 2. Histogram - Corpus. Note: Histogram containing most popular terms within the corpus.
Fig. o. mistogram - ti-1atr. Note: Histogram containing most popular terms within the corpus, with tf-idf weighting.
Fig. o. mistogram - ti-1atr. Note: Histogram containing most popular terms within the corpus, with tf-idf weighting.
Top 6 Frequent Words in dtm. Note: top six most frequent words with their corresponding frequencies. Table 3
Top 6 Frequent Words in dtm. Note: top six most frequent words with their corresponding frequencies. Table 3
Fig. 8. Correlation Map Using dtm - Simple Counting Weighting Scheme. Note: The vertical axis represents the heights.
Fig. 8. Correlation Map Using dtm - Simple Counting Weighting Scheme. Note: The vertical axis represents the heights.
Fig. 9. Correlation Map Using dtm - tf-idf Frequency. Note: The vertical axis represents the heights.
Fig. 9. Correlation Map Using dtm - tf-idf Frequency. Note: The vertical axis represents the heights.
Topic-relevant Words - Topics 1 to 6. Note: Terms with the highest probability of appearing in Topics 1 through 6. Table 6
Topic-relevant Words - Topics 1 to 6. Note: Terms with the highest probability of appearing in Topics 1 through 6. Table 6
Note: Terms with the highest probability of appearing in Topic 1 and Topic 2. Table 5
Note: Terms with the highest probability of appearing in Topic 1 and Topic 2. Table 5
aie pI a aM a Notes: Heatmap for documents published in 1999 (tf weighted dtm). The color key corresponds to probabilities for each topic being discussed during the corresponding interest rate decision meeting. monies nnn eens | See Se
aie pI a aM a Notes: Heatmap for documents published in 1999 (tf weighted dtm). The color key corresponds to probabilities for each topic being discussed during the corresponding interest rate decision meeting. monies nnn eens | See Se
Fig. 13. Sentiment - Word Count. Notes: Scaled count of positive (top panel), negative (middle panel), and uncertainty (bottom panel) words in each document using the dictionary approach.
Fig. 13. Sentiment - Word Count. Notes: Scaled count of positive (top panel), negative (middle panel), and uncertainty (bottom panel) words in each document using the dictionary approach.
AR GI SSIES Lr, en Se Note: This figure represents how much each term contributes to the sentiment in each corresponding category. These categories are defined as mutually exclusive. Constraining (top left), positive (top right), negative (bottom left), and uncertainty (bottom right) sentiments are represented.
AR GI SSIES Lr, en Se Note: This figure represents how much each term contributes to the sentiment in each corresponding category. These categories are defined as mutually exclusive. Constraining (top left), positive (top right), negative (bottom left), and uncertainty (bottom right) sentiments are represented.
Fig. 14. Sentiment Indicator — Dictionary Approach.
Fig. 14. Sentiment Indicator — Dictionary Approach.
Note: Words with the highest probability of appearing in Topics 1 through 4. Topic-relevant Words — Topics 1 to 4. Table 7
Note: Words with the highest probability of appearing in Topics 1 through 4. Topic-relevant Words — Topics 1 to 4. Table 7
Fig. 16. Probability Distribution — Topics 1 to 4 from 2007 to 2008. Note: The color key corresponds to probabilities for each topic being discussed during the corresponding interest rate decision meeting
Fig. 16. Probability Distribution — Topics 1 to 4 from 2007 to 2008. Note: The color key corresponds to probabilities for each topic being discussed during the corresponding interest rate decision meeting
‘ig. 17. Probability Distribution — Topics 1 to 4 from 2008 to 2009. Vote: The color key corresponds to probabilities for each topic being discussed during the corresponding interest rate decision meeting.
‘ig. 17. Probability Distribution — Topics 1 to 4 from 2008 to 2009. Vote: The color key corresponds to probabilities for each topic being discussed during the corresponding interest rate decision meeting.
g. 18. Probability Distribution — Topics 1 through 4 from 1999 to 2000. te: The color key corresponds to probabilities for each topic being discussed during the corresponding interest rate decision mee
g. 18. Probability Distribution — Topics 1 through 4 from 1999 to 2000. te: The color key corresponds to probabilities for each topic being discussed during the corresponding interest rate decision mee
Related topics:
Natural Language ProcessingMachine LearningSentiment AnalysisR programming languageEuropean Central BankFile ReplicationCentral Bank Communication
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