Category: Uncategorized

Currently, my project is almost done, I just need to add in the written analyses for each album. I also need to make a short about page for myself, and possibly play around with the colors and theme. It is fascinating how I can input things on Omeka that look like a bunch of codes and random data, and then end up with a professional looking website. It has been really cool to learn about how to use Omeka, I have never used a program like this before. I feel like this is a valuable skill to have as I get into more upper level research, now I have this as an available resource to create projects in.

On my site, I am currently adding visuals of distinctive words in each song, frequency of most common words by song, and a world cloud of the most frequent words in the overall album. I am doing this for each individual album on its own page, and this will help users to visualize my data. Then, to allow them to further understand, I have a caption under each figure and am adding a short written analysis on each of these pages to explain what this data means in the context of her albums. I particularly like the visual that is the distinctive words in each song for an album, I feel that this best gets the message across of the overall themes present in that particular album. The most common words in the entire album are sometimes general, so sorting it by distinctive words in each song provides an even better description. For viewers to understand this, they will just have to view all the pieces together as a whole- the figures, what they mean, and the written analysis. If users have no background on Taylor Swift, this might make it a little more difficult to understand, but I hope that my written analysis can provide them the information they need to comprehend the data sets.

For my final digital humanities project, I am really happy with my progress so far. As of now, I have all of my items created in Omeka, all of my data cleaned, and some of the data analyzed. I have put some of the albums into voyant to obtain the figures that I am going to use in my final site, but still need to finish the rest of the albums. Though this is slightly time consuming, I think it will be worth it to have a really well fleshed out project. Also, since I didn’t have to spend much time cleaning the data, I don’t mind a little extra time spent on the analysis. I am really excited to start inputting things into voyant and making it aesthetically pleasing. I hope to have each albums’ page correlate to the colors/themes of that album, since each of her albums have pretty distinct colors and overall themes.

The data that I have cleaned is song lyrics. Luckily, this process hasn’t been too difficult, thanks to Taylor Swift’s huge fan base. I was able to find all of the lyrics to each album already on one big document, as well as lyrics separated by album as downloadable files. This made it easy for me to get a document for each album that includes every lyric from that specific album. Cleaning this data was satisfying, as I felt like I was very easily able to compile a large amount of data ready for analysis. One issue that did come up was that the downloadable files that were separated by song had credits at the top of each of them in different languages, which sometimes made a random word high in frequency. But, I was able to troubleshoot this by adding some of those words to the stop list on voyant. Similarly, on these files, the words “chorus” “verse” and “bridge” were included in each song denoting each portion of the song. So, as I have been beginning my analysis these words, especially chorus, have been coming up as high in frequency. Again, I just have added them to the stop list on voyant to fix this. I am going to visualize this data by having a different link/section for each album on my website, complete with song by song analysis of frequent words and an overall word cloud and voyant image for the album as a whole. This will provide a lot of visuals for each album. I also hope to upload all albums to into voyant, and then do one visual that compares them all to eachother.

The data that I have collected is a collection of all the lyrics of Taylor Swift’s songs across her various albums. This includes a document for each of her twelve albums with all of that album’s lyrics on it, as well as files with the plain text version of each song. The data is sourced from Reddit and Kaggle, where users had already separated these songs and organized them by album. From both of these sources, I have all of Taylor Swift’s lyrics, organized in two different ways for analysis (song by song for each album, and by album as a whole). With this data, someone could learn about the general themes across Swift’s different albums from the frequency of words per song in each album. One could also learn about her lyricism and word choice through this data, and how this may have changed over time throughout her many eras. As for general trends seen before my in depth analysis, I definitely see that her later albums have richer lyrics, with more profound words showing up than in some of her earlier songs. I have also noticed that her albums greatly vary in length, some have around ten tracks, while others have close to thirty. This has to do with some albums having deluxe versions or extra songs from her re-recordings, and others not having these features. 

The data that I’m gathering is song lyrics, specifically those of all of Taylor Swift’s albums. So far, this process has not been too difficult, because I was able to find a document with all of the song lyrics organized by album. So, all I had to do with these is copy and paste the lyrics over to new documents such that each album has its own document. From here, I just used the “find and replace” option on google docs to delete “Taylor’s Version” and “From the Vault” from the song titles, since these words are not part of the themes of the album, they are just further describing the title. I also have found downloadable files of each specific song’s lyrics, organized by album. So, I don’t have to do any cleaning on these which is super nice. As for my general observations, I have noticed that there are some albums that are notably longer than others, which might impact my results just because there is so much more text in albums such as Red Taylor’s Version, which has a ton of tracks, including one that is ten minutes long. I will keep this in mind when doing my analysis. The only remaining data to collect is to finish putting all of the lyrics on their respectable google docs, I have around three documents remaining. Also, I need to decide upon which albums I am going to use the downloadable files for. I am going to use these files to do a song by song analysis on a few of the albums, I just need to decide which albums will work best for this type of analysis. I am going to finish the documents tonight, and then with the work time provided next class I will decide upon which albums to use for the song by song analysis.

For my final project, I have chosen to base it on Taylor Swift’s lyrics across all of her albums. This topic intrigues me because I am a huge Taylor Swift fan, and I think she has really lyrically rich music. These lyrics are all incredible, but definitely vary from album to album, or “era to era.” So, I think it will be really interesting to see the differences in lyrics throughout all of her different albums. I already know all of her songs, but I want to learn how each album as a whole compares to the others. This will allow me to take a broader look at the lyrics that is difficult to do by just listening to her songs. I think that this project can show others just how impressive her lyrics are in her songs, many people think that she is super surface level, but I’m hoping to be able to demonstrate some of the depth of her albums. Hopefully, a resource like this that looks at the albums as a whole, not just through the lens of her most popular hits, can help to demonstrate this.

Publishing edits on Wikipedia was a new experience for me that provided me with a lot of insight on public research. I found working on a resource for the public to be really engaging, as I knew I was working on something that truly mattered and had the potential to make an impact. I enjoyed knowing that my work would be available to anyone who wanted to learn about Jenny Levy and women’s lacrosse. Women’s lacrosse definitely lacks proper recognition in many cases, so I love that I had the opportunity to expand the information available about this sport. From my research, I learned that Jenny Levy has many more accolades and achievements than I had previously thought. She has led multiple teams to extremely impressive seasons, and has earned a huge amount of awards as a result of her success. Additionally, I learned that she was a dominant player in her time, leading her team to their first ever national championship. I’m glad I got to add a lot of these achievements to the article, they are impressive feats that deserve recognition. Throughout the process of editing my article, I learned about the inner workings of Wikipedia, which I hadn’t known before. Since I have usually been told by my teachers to stay away from Wikipedia, this was the first time I had ever truly explored the site. I never knew about the talk page, and how edits are often taken down and changed by other users. The community element of Wikipedia was brand new to me, as the sheer amount of editors that are constantly changing and improving articles on a multitude of topics fascinated me. Another new aspect of this editing process was the concept of plagiarism in these edits. While I knew that I could not copy someone else’s words very closely or quote someone else without any citation, I was unaware that things like similar sentence structure have the potential to be flagged as plagiarism. In the past, I had thought that replacing words with synonyms would suffice for most cases, but now I know that taking information from a source requires much more than that. So, this was a part of the project that I had trouble with at first, I didn’t know how to put what I was reading into my own words in a way that would be acceptable to Wikipedia. However, the modules that demonstrated examples of plagiarism versus paraphrased work were very helpful and allowed me to gain a better understanding of how to compile my information for my article. I felt that physically making the edits to the Wikipedia article was much easier than I had assumed it to be, between the modules and in class instruction, the site was relatively easy to navigate. The difficult part was at the beginning of the process, when I had to decide what needed to be added to the article. Since my article was a stubs class and had little information, it was almost overwhelming how much information could have been added. But, after looking through similar articles about other collegiate coaches and seeing what general information these articles had, I was able to narrow down a couple of categories that I could focus on adding to. Overall, this project provided me with a fresh perspective on Wikipedia, it is no longer a website I feel the need to avoid, but one that I am eager to revisit. 

Link to article: https://en.wikipedia.org/wiki/Jenny_Levy

Currently, my Wikipedia article is in the sandbox, edited with the changes I wanted to make. I added two paragraphs, added a few subheadings to make the article more clear and organized, and updated some outdated information present in the article. All that there is left to do is to get my edits proofread by my peers to make sure that they all make sense, and then my edits should be good to go. I am feeling pretty good about the article, I think that the information that I added was beneficial to the article and truly will improve it. Even though this is just one article that I am editing, I feel like this is a small step in the right direction in terms of bringing more representation to women’s lacrosse. I have learned a lot about my topic, which is the head women’s lacrosse coach, Jenny Levy, at UNC. I hadn’t realized all of the amazing accolades that she has, both as a player and a coach. I figured that she would have impressive statistics as a coach of a historically standout program, but I had no idea how great of a player she was. She was a top performer at UVA and led them to their very first championship, which is really cool. I have also learned that Wikipedia is not comprehensive on every topic, and there are many topics that need significant editing. There are also many sources that can provide valuable information that is not on Wikipedia. This really shows me how the internet and the information we have on it can always expand, and it ever changing.

The recent Wikipedia project is teaching me a ton about Wikipedia as a whole. I had always known of Wikipedia as an unreliable source that I was advised to stay away from, so I was surprised by some of the training modules on plagiarism and AI. These modules opened my eyes to just how strict Wikipedia is on AI use and plagiarism, there are many policies in place to ensure that edits are not copied from another author’s work. While I knew not to closely copy authors’ words and call them my own, I didn’t realize that even similar sentence structure can qualify for plagiarism. I now know to be very mindful of this when making my own edits after spring break!