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config.yaml
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config.yaml
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sparkSettings:
appName: "Twitter-Opinion-Mining"
executorMemory: "10g"
driverMemory: "8g"
input:
tweets: "wasb://[email protected]/"
training: "wasb://[email protected]/twitter_training.csv"
stopwords: "wasb://[email protected]/stopwords.txt"
output:
csvOutputPath: "wasb://[email protected]/output.csv"
hostname: "aaltoms"
database: "ms_twitter"
user: "aaltoadmin@aaltoms"
password: "fD^bS7yVGw"
encrypt: true
trustServerCertificate: false
hostNameInCertificate: "*.database.windows.net"
loginTimeout: 30
table: "twitter_opinions"
topicModelling:
i: 20 # The number of iterations performed by the topic modelling
k: 3 # The number of topics to be generated by the topic modelling
n: 10 # The number of words generated per topic
wnd: 5 # The topic modelling window, i.e. the number of time intervals that are to be considered part of a peak. E.g. if there is a peak at t=10, and tmWnd is 3, tweets will be analyzed from t=9 to (and including) t=11. NB! Must be an odd, positive number.
peakDetection:
#primaryFilter:
#secondaryFilter:
alpha: 5 # The roughness of the primary peak detection filter (defines moving average window size)
beta: 12 # The roughness of the secondary peak detection filter (defines moving average window size). By definition, beta > alpha.
#delta_gen: