Run your first generic and targeted sentiment analyses using a dataset of US presidential concession speeches.
Visualize sentiment analysis results over time in a plot while stratifying by an additional variable
In this guided project, you will learn how to import textual data stored in raw text files into R, turn these files into a corpus (a collection of textual documents), and tokenize the text all using the R software package quanteda. You will then learn how to check for words with positive or negative sentiment within the text, and how to plot the proportion of use for these words over time, while stratifying by a third variable. You will also learn how to carry out a targeted sentiment analysis by looking for words with a positive or negative sentiment that are adjacent to relevant keywords or phrases, and how to compare the results of a targeted sentiment analysis with the results of a generic analysis.
statistical programming
Statistical Classification
Sentiment Analysis
Text Corpus
Rstudio
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Load text documents into R studio, convert a number of text documents into a corpus, and extract data from text document file names and add them to a new column in a dataframe.
Split up a text document corpus into tokens, or individual words and punctuations. Check for words in the data that have positive or negative sentiment using the Sentiment Dictionary.
Plot the proportion of positive and negative words over time while stratifying by a third variable.
Carry out a targeted sentiment analysis by looking for words with a positive or negative sentiment that are adjacent to relevant keywords.
Compare the sentiment for both generic and targeted sentiment analyses while stratifying by a third variable, plotting the results over time.
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In a split-screen video, your instructor guides you step-by-step
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