## 2 Literature Review
### Machine Learning Applications in Cultural Analytics  
A rising amount of research has addressed content or statistical analysis, social media emotion recognition, etc., but less so in the arts. [PERSON] coined \"cultural analytics\" in 2007. This is not a strict or confined term because it can be adapted. As a concept, it includes \"the use of computational and design methods-- including data visualization, media and interaction design, statistics, and machine learning--for exploration and analysis of contemporary culture at scale\" [1]. Therefore, we use the concept as a basis for our research. [PERSON] et al. [76] used [PERSON]'s psychological model to classify Instagram posts' sentiments. Other research has employed Naive Bayes (NB), Support Vector Machines (SVMs), Decision Trees (DTs), Random Forest (RF), Convolutional Neural Networks (CNNs), BiLSTM, L2, and Adam on large-scale Twitter data to detect pandemic emotions [77]. Using Instagram and Twitter data, we found in our most recent paper [78] that a public art installation that elicits intense emotions may gain popularity due to deliberate media-sharing activities and habits.  
There is a vast amount of literature on ranking methods. Most studies ranked products, websites, news, or services. Recommendation systems also use ranking. [PERSON], [PERSON], and [PERSON] [79] ranked products online. They developed a novel approach using a hesitant fuzzy set (HFS) and a sentiment work framework to calculate overall performance. [PERSON] et al. [80] rated social media news feeds using the Random Forest (RF) algorithm, providing personalized and non-personalized prediction models. They tested the model on Twitter data to forecast news feed relevance. Non-personalized models outperformed personalized ones. Based on social media engagement, [PERSON] and [PERSON] [81] ranked US Olympic athletes as brand endorsers (likes, comments, total number of followers, etc.). They examined the link between follower engagement and brand mentions. Using text analysis of the tweets of 190 US Olympus during the 2018 PyeongChang Games, researchers revealed significant gender differences in engagement. Additionally, it was revealed that non-brand-related posts generated far more interaction than brand-related posts.  
We previously discussed popularity. Then we examined popularity ranking. [PERSON] et al. [82] combined a personalized score and the news popularity score to produce a ranking score that can provide users with personalized news and predict their popularity in real time. [PERSON] and [PERSON] [83] developed a novel approach to analyzing and visualizing research datasets; the number of citations from the year of origin, overall number of citations, and impact factor of journals that publish articles citing datasets were used. A system to obtain datasets based on ranking variables was also recommended. In our recent study [84], we examined Instagram art memes and museum posts to assess the impact and popularity of classical art. Using supervised machine learning algorithms and emotion analysis, we identified the most prevalent emotions in each scenario. Then, we found the most popular artworks as memes and museum posts in the context of aesthetic experience using the LightGBM ranking algorithm and relevance scores in each group of artworks (91 total, \(N=1222\) for memes and \(N=3304\) for museum posts). Our tests revealed the top seven art memes, top twelve museum artworks posts, and top four common artworks in both cases.