# State of Industry 5.0--Analysis and Identification of Current Research Trends
## 3 Data Analysis and Discussion
### Topic Analysis  
Topic Analysis was used to understand the major themes around which the published literature on Industry 5.0 can be classified. Topic analysis from text mining is defined as the act of extracting topics or thematic elements from a given set of documents. Topic analysis  
Figure 2: Terms usage trends in the context of Industry 5.0.  
focuses on the characterization of a topic based on distribution of terms and the mixture of each topic in a document [25]. One of the most common methods of topic analysis from text mining is \"Latent Dirichlet Allocation\" (LDA) [26]. LDA is an unsupervised machine learning method mostly used for applications such as opinion modeling, extracting topics from source codes, and hashtag recommendations [17]. One of the greatest advantages of LDA is its pertinence to several domains while taking three domains into consideration, documents, words, and topics. LDA enables the user to define the number of topics as a parameter that the textural data must be characterized into. It is to be noted that, if this parameter is small, the topic identified provide only few semantic contexts whereas when the parameter to too large there is a scope for the topics identified to overlap. This, after several trails the authors limit the number of topics the data is to be characterized into 5 to be more reflective and coherent to Industry 5.0. Table 4 depicts the top five topics, and ten most likely terms observed in each topic, and a representative label for each topic provided by the authors. Figure 3 represents the topic distribution across the entire dataset of 196 abstracts on Industry 5.0 and Figure 4 portrays the spread of the identified topics among the data gathered over the period of years 2016 to 2022. The top abstracts, in a descending order, that represent each individual topics (Table 4) have been summarized to explore and better understand the perspective in the context of Industry 5.0 being addressed so far through published articles  
_Theme 1--Industry 5.0 in context of supply chain evaluation and optimization in manufacturing processes_: This topic supports the exploration of how Industry 5.0 can enable supply chain evaluation and optimization in manufacturing processes. More specifically, use of a multi-objective mathematical model to design a sustainable-resilient supply chain based on strategic and tactical decision levels [27], optimization of mining methods using multi-level, multi-factor, multi-objective, and multi-index comprehensive evaluation system involving technology, economy, and safety [28], exploring Social Value Orientation theory for understanding decision making preferences for join resource allocation [29], exploring the constructs of Industry 5.0 for supporting supply chain operations [22], research directions for supply chain transformations [30], influence of industrial internet of things and emerging technologies on digital transformation capabilities of organizations [31], effectiveness indicators for enterprise resource planning systems to aid digitization of information flows [32], enabling constructs of Industry 5.0 to control and manage supply chains in emergency [33], future of supply chains in context of Industry 5.0 [34], and approaches for supply chain digitization [35]. Addressing the 13% of the data gathered, a constant interest among the researchers is observed over time on this topic. Figure 4 portrays this trend. _Theme 2--Industry 5.0 in context of enterprise management, innovation, and digitization_: Composed of 27% of the data analyzed, this thematic topic addresses the construct of Industry 5.0 in context of Enterprise Management, Innovation, and Digitization.