Analyzing the Script of Kung Fu Panda
As my experience with Digital Humanities projects expands, my inspiration for this project stems from my appreciation of films, scripts, and text analysis. When browsing films I could center my project around, I stumbled across a classic children’s movie I loved growing up, Kung Fu Panda. Its light-hearted nature and wittiness with the message of perserverance made the film a memorable staple of childhood media for me. I explored Voyant text-analysis tools in greater depth throughout this project, and have attatched various embedded graphs for readers to visualize the data I gathered from the Kaggle CSV in which the movie script was found.
The word cloud text analysis tool is especially useful for users who want to see text frequency specifically in a visual manner. This adjustable word cloud allows for users to increase or decrease the number of terms using a slider, and the cooresponding graph will adjust according to the term amount selected. In the film, Kung Fu Panda, the names of central characters appear the most. For example, Po is the main protagonist of the film, which is understandably why his name appears the most in the chart. Other characters like Shifu and Oogway appear fewer times, but still drive the plot significantly, so they are mentioned many times as well.
Additionally, another useful text analysis tool that Voyant provides is the a linking terms diagram. This tool helps a user visualize words in connection to one another throughout a text. This may be useful for analyzing words used several times together in dialogue, therefore indicating plot devices used throughout the film. Words such as “noodles” can be seen directly connecting to the main character’s name, “Po,” indicating that there is a connection between noodles and the main character in the film.
This interactive line and bar graph indicates the highest occuring words across the film in chronological order. This kind of text analysis indicates a change in word frequencies with each document segment, which additionally changes as the film progresses. Trends such as how many of the characters of the film are introduced after the opening sequence can be seen when the term “Po” is the only major term present for the first segment of the script document.
In conclusion, this assigment allowed me to fully dive into the possibilities of text analysis. For any sort of media analysis project, tools such as these would be incredibly useful for furthering any claims made regarding text correlations and trends. Though this blog post does not actually evaluate any textual trends in great detail, the ability to customize the data tools and report the trend findings could substantiate many textual arguments regarding any element of stories a user wants to analyze. Overall, this project was enriching in that I could expand upon a field I found previously intruiging earlier in my Digital Humanities course.
