Model Creation and Analysis Sample Clauses

Model Creation and Analysis. The field of interest for the sentiment analysis is the major feedback contained within the one-year survey. Alumni were asked to provide any comments that related to their major field and/or department. This feedback field was completely open to any length or words that the student provided. Initial analysis and visualization of the major feedback field occurred through word clouds. First, the feedback field from those who responded to this question were merged and transformed into a document term matrix. After aggregation, this matrix contained each individual word as the column headers, with the rows being the grouping of interest, and the values being the word count. There were two groupings of interest that were used throughout this analysis. The first is by graduation year. This is to explore the difference in sentiments between students of different graduating years. The second is by the response to “Would you pick Etown again if you started your college search over today?” The options for this field were “Definitely Not”, “Probably Not”, “Not Sure”, “Probably”, and “Definitely”. Once the document term matrix was created, it was converted into a dictionary in which the individual word became the key and the number of occurrences of the word was the value. A common English stop words list was imported and these words were eliminated from the feedback dictionary. These stop words are commonly used words such as “a”, “the”, “is”, and “are” which provide little useful information. For grouping by graduation year, if a word was within the top occurring words of all three years (2017, 2018, and 2019) it was added to the list of stop words that were then removed from the feedback analysis. The commonly shared words were removed from the analysis as the difference in the sentiments by graduation years is the focus of the analysis. Therefore, removing the commonalities allows the variation between years to shine through. For grouping by if they would pick Etown again, if a word was within the top occurring words of at least 3 of the 5 responses it was added to the list of stop words that were then removed from the feedback analysis. The commonly shared words were removed from the analysis as the difference in the sentiments by response to this question is the focus of the analysis. Therefore, removing the commonalities allows the variation between response groupings to be shown. Once the feedback is parsed, organized, and filtered, the information is organiz...
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