สวัสดีฉันชาโน่

สวัสดีฉันชาโน่

ผลลัพธ์ (ไทย) 2:

Hello, I'm Shana

ผลลัพธ์ (ไทย) 3:

Figure 2 plots the distribution of the word ‘emission’ in the top panel and topic 10, which we identified as ‘carbon emission.’ It is clear that the word ‘emission’ also appears frequently in topics 4, 15, 17, and to a lesser extent, topics 2, 11 and 19 as well. This result confirms that the LDA algorithm recognizes the polysemy or contextual nature of words by assigning the same word to multiple topics. However, a closer inspection of these topics reveals that the word list patterns for most of the topics mentioned above are less clear-cut as compared with topic 10. Indeed, we noted later that topic 15 is related to efficient energy in general, while topic 4 strongly features supply chain sustainability, while word lists for topics 2, 11 and 19 do not present any coherent theme. We have also investigated several validation tests for LDA topic outputs as proposed in Huang et al. (2017).