Basic, Elementary, Flexible Social Media Sentiment Analysis In R

James Cook from the University of Maine at Augusta introduces the concept of conducting basic sentiment analysis using R, a method applicable to various content types. He discusses the history of sentiment analysis, tracing back to the early 2000s and 1997 for algorithmic sentiment analysis of texts. The conversation then shifts to the development of social science sentiment analysis in the 20th century, highlighting the work of Phillip Stone and colleagues in 1962, which involved creating the General Inquirer system and a Harvard dictionary of sentiments. The General Inquirer system is still widely used and cited in research.

The discussion moves to updating sentiment analysis methods using R, a free and open-source software package, and rstudio, an integrated development environment (IDE) that provides a consistent experience across platforms. James Cook explains the use of various packages for extracting content from social media, such as Reddit and YouTube, and for conducting sentiment analysis using the General Inquirer dictionary. He also introduces the concept of visualizing data with ggplot and igraph packages, emphasizing the versatility of sentiment analysis across different text sources.

James Cook details the process of preparing data for sentiment analysis, emphasizing the importance of cleaning and formatting text data. He explains the need to remove special characters and emoticons that could confuse the sentiment analysis package and to ensure that counts, such as reply and like counts, are in the correct numeric format. The focus is on structuring the data to optimize its analysis and interpretation.

The paragraph describes the process of running sentiment analysis on YouTube data using R. James Cook demonstrates how to generate sentiment scores, bind them with existing data, and visualize the results using histograms and scatter plots. He also discusses the potential relationships between sentiment and other features of comments, such as the number of replies or likes a comment receives.

James Cook continues to explore data visualization techniques for sentiment analysis, focusing on creating histograms, scatter plots, and density plots. He explains how to use ggplot2 and ggpmisc packages to add statistical analysis to plots, such as best fit lines and equations. The aim is to provide a comprehensive visual representation of sentiment data, revealing patterns and trends within the comments of a YouTube video.

The speaker investigates potential relationships between sentiment scores, like counts, and reply counts in YouTube comments. Using scatter plots and density plots, he examines whether certain sentiment scores are associated with higher like counts or more replies. The analysis reveals that while there is a slight positive relationship between like counts and sentiment, it is not very strong or significant. A more robust relationship is found between reply counts and like counts, suggesting that comments that receive more replies also tend to receive more likes.

James Cook extends the sentiment analysis to Reddit data, using Reddit extractor to gather information from the Tar Heels subreddit. He demonstrates how to prepare and analyze the data, focusing on the content of the comments. The analysis includes creating histograms and scatter plots to understand the sentiment distribution and exploring the relationship between word count and sentiment scores in comments.

The speaker introduces a bimodal semantic network based on a non-monogamous subreddit, aiming to understand how individuals feel about different combinations of sexual and gender identities. He describes the process of generating the network and the associated data, then explains how to visualize the network using igraph. The goal is to color-code the network nodes based on the sentiment of the associated words, providing a visual representation of the sentiment distribution across different identities.

James Cook details the process of color-coding the nodes in a bimodal semantic network according to the sentiment analysis of the associated words. He explains how to retrieve the sentiment scores for each word, assign them as properties to the nodes, and then plot the network with color-coded nodes. The resulting visualization shows a distribution of positive, negative, and neutral sentiments across different positions within the network, offering insights into the language and perceptions associated with each identity.

In the final paragraph, James Cook encourages viewers to apply the techniques discussed to their own data and areas of interest. He suggests experimenting with different data sets and variables to gain new insights. The speaker emphasizes the value of sentiment analysis in understanding and visualizing data, and encourages further exploration and learning.