The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

Sum-up: Utilizing Automated Text Analysis to Support Interpretation of Narrative Feedback

Combine topic modeling and sentiment analysis to identify individual students’ gaps, and highlight their strengths and weaknesses across predefined competency domains and professional activities.

This function runs a series of text processing and analysis steps including text cleaning, tokenization, lemmatization, topic modeling, and sentiment analysis. It then classifies sentences into topics and generates an output summarizing the results.

This function performs the following steps: - Cleans the input text data using text_clean. - Tokenizes the text into sentences and removes stopwords. - Lemmatizes and annotates the sentences using a UDPipe model. - Counts word frequencies and excludes stopwords. - Performs topic modeling on the word counts. - Runs sentiment analysis based on the specified method (Grasp or SentimentR). - Classifies sentences into topics using the topic classification model. - Generates output summarizing the topics and sentiment.

library("sumup")

data(example_data)
ex_data <- example_data
ex_settings  <- set_default_settings()
ex_settings  <- update_setting(ex_settings , "language", "en")
ex_settings  <- update_setting(ex_settings , "use_sentiment_analysis", "sentimentr")
result <- run_sumup(ex_data, ex_settings )

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.