Overview

Kickstarter is an American public-benefit corporation based in Brooklyn, New York, that maintains a global crowd funding platform focused on creativity. The company’s stated mission is to “help bring creative projects to life”.

Kickstarter has reportedly received almost $6 billion in pledges from 20 million backers to fund more than 200,000 creative projects, such as films, music, stage shows, comics, journalism, video games, technology and food-related projects.

Data

The dataset for this assignment is taken from webroboto.io ’s repository. They developed a scrapper robot that crawls all Kickstarter projects monthly since 2009. I noticed that the most recent crawls appear to be incomplete, so we will take data from the last complete crawl on 2021-05-17.

The data is contained in the file kickstarter_projects_2021_05.csv and contains about 131k projects and about 20 variables.

Tasks for the Assignment

1. Identifying Successful Projects

a) Success by Category

There are several ways to identify success of a project:
- State (state): Whether a campaign was successful or not.
- Pledged Amount (pledged)
- Achievement Ratio: The variable achievement_ratio is calculating the percentage of the original monetary goal reached by the actual amount pledged (that is pledged\goal *100).
- Number of backers (backers_count)
- How quickly the goal was reached (difference between launched_at and state_changed_at) for those campaigns that were successful.

Use two of these measures to visually summarize which categories were most successful in attracting funding on kickstarter. Briefly summarize your findings.

I chose achievement ratio and the number of backers to identify success of a project. I grouped all projects by categories. Each graph shows the ranking of successful projects when ranked based on achievement ratio and the number of backers. When measured by acheivement ratio, projects in comic and fasion was the most successful, whereas when measured by the number of backers(supporters), music, film and publishing were the most successful categories of the projects. To conclude, we can observe that projects in media and entertainment are steadily successful.

2. Writing your success story

Each project contains a blurb – a short description of the project. While not the full description of the project, the short headline is arguably important for inducing interest in the project (and ultimately popularity and success). Let’s analyze the text.

a) Cleaning the Text and Word Cloud

To reduce the time for analysis, select the 1000 most successful projects and a sample of 1000 unsuccessful projects (by a metric of your choice). Use the cleaning functions introduced in lecture (or write your own in addition) to remove unnecessary words (stop words), syntax, punctuation, numbers, white space etc. Note, that many projects use their own unique brand names in upper cases, so try to remove these fully capitalized words as well (since we are aiming to identify common words across descriptions). Create a document-term-matrix.

Provide a word cloud of the most frequent or important words (your choice which frequency measure you choose) among the most successful projects.

Success1000
## <<DocumentTermMatrix (documents: 1007, terms: 4402)>>
## Non-/sparse entries: 10169/4422645
## Sparsity           : 100%
## Maximal term length: 36
## Weighting          : term frequency (tf)
## <<DocumentTermMatrix (documents: 1000, terms: 4485)>>
## Non-/sparse entries: 10634/4474366
## Sparsity           : 100%
## Maximal term length: 71
## Weighting          : term frequency (tf)

b) Success in words

Provide a pyramid plot to show how the words between successful and unsuccessful projects differ in frequency. A selection of 10 - 20 top words is sufficient here.

## Warning: Removed 3 rows containing missing values (position_stack).

c) Simplicity as a virtue

These blurbs are short in length (max. 150 characters) but let’s see whether brevity and simplicity still matters. Calculate a readability measure (Flesh Reading Ease, Flesh Kincaid or any other comparable measure) for the texts. Visualize the relationship between the readability measure and one of the measures of success. Briefly comment on your finding.

3. Sentiment

Now, let’s check whether the use of positive / negative words or specific emotions helps a project to be successful.

a) Stay positive

Calculate the tone of each text based on the positive and negative words that are being used. You can rely on the Hu & Liu dictionary provided in lecture or use the Bing dictionary contained in the tidytext package (tidytext::sentiments). Visualize the relationship between tone of the document and success. Briefly comment.

b) Positive vs negative

Segregate all 2,000 blurbs into positive and negative texts based on their polarity score calculated in step (a). Now, collapse the positive and negative texts into two larger documents. Create a document-term-matrix based on this collapsed set of two documents. Generate a comparison cloud showing the most-frequent positive and negative words.

## NULL

c) Get in their mind

Now, use the NRC Word-Emotion Association Lexicon in the tidytext package to identify a larger set of emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, trust). Again, visualize the relationship between the use of words from these categories and success. What is your finding?

## Warning in sentiment == c("joy", "positive", "negative", "fear"): longer object
## length is not a multiple of shorter object length