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Analysis of two TED talks ¦ 2022 Best answer

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Analysis of two TED talks ¦ 2022 Best answer You will create a document showcasing your analysis of two TED talks and presenting a list of 10 qualities, techniques and/or presentation skills that made the presentations you watched inspiring, captivating, creative and effective Analysis of two TED talks You will create a document showcasing your analysis of two TED talks and presenting a list of 10 qualities, techniques and/or presentation skills that made the presentations you watched inspiring, captivating, creative and effective (from your own perspective). The document should include an analysis and images for each TED Talk. Sentiment Analysis of TED Talks-Bo Hawkes Introduction TED, which is an acronym standing for Technology, Entertainment and Design, “is a nonprofit devoted to spreading ideas.” A powerful way that TED works to achieve this mission of spreading ideas is through their TED Talks. TED Talks are short presentations on any topic imaginable. The common link, however, is that every TED Talk seeks to educate listeners on something new. https://www.ted.com/about/our-organization This project aims to analyze whether there is a correlation between the positive/negative sentiment of a TED Talk, and the popularity of the talk. Hypothesis I predict that the sentiment in the top five most viewed talks will be more positive than negative. Conversely, the sentiment of the top five least popular TED Talks is negative. Viewers want to listen to TED Talks that are positive. Setup In order to run all of my analysis functions, I first had to run the necessary packages. library(ggthemes) library(ggplot2) library(wordcloud2) library(tidyverse) library(stringr) library(tidytext) library(textdata) After downloading the required packages, I then imported two datasets from Kaggle. The datasets from Kaggle include every TED Talk up until September 21, 2017. Below is the main dataset https://www.kaggle.com/rounakbanik/ted-talks?select=ted_main.csv Below is the dataset that includes the transcripts https://www.kaggle.com/rounakbanik/ted-talks?select=transcripts.csv Data Import tedMain <- read.csv("~/Desktop/ted_main.csv", stringsAsFactors=FALSE) tedTranscripts <- read.csv("~/Desktop/transcripts.csv", stringsAsFactors=FALSE) After importing the two datasets, I then merged them. tedTalks top5views top_n(tedTalks, -5, views) -> bottom5views Top Ten Then, I ensured that, from the top and bottom viewed TED Talks, the ten most popular words were extracted. Before utilizing sentiment analysis on the five most popular words in each category, I wanted to see the top ten most popular to gain a larger understanding before closing in on five. top5views %>% unnest_tokens(word, transcript) ->top5words top5words %>% count(word, sort = TRUE) %>% anti_join(stop_words) %>% arrange(desc(n)) %>% head(10) %>% ggplot(aes(reorder(word, n), n)) geom_col() coord_flip() theme_calc() bottom5views %>% unnest_tokens(word, transcript) -> bottom5words bottom5words %>% count(word, sort = TRUE) %>% anti_join(stop_words) %>% arrange(desc(n)) %>% head(10) %>% ggplot(aes(reorder(word, n), n)) geom_col() coord_flip() theme_calc() Filter In order to run sentiment analyses with Afinn, Bing, and NRC, I first had to import the data sets, unnest the tokens, and filter out unnecessary words. top5views %>% unnest_tokens(word, transcript) %>% anti_join(stop_words) %>% filter(!word %in% c(“laughter”, “la”, “music”, “ha”)) -> top5WordsFiltered

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