Introduction to Sentiment Analysis in R with quanteda

提供方
在此指导项目中,您将:

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

2 hours
初级
无需下载
分屏视频
英语(English)
仅限桌面

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.

您要培养的技能

  • statistical programming

  • Statistical Classification

  • Sentiment Analysis

  • Text Corpus

  • Rstudio

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. 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. 

  2. 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. 

  3. Plot the proportion of positive and negative words over time while stratifying by a third variable. 

  4. Carry out a targeted sentiment analysis by looking for words with a positive or negative sentiment that are adjacent to relevant keywords.

  5. 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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