## 4 Results
### Public Perceptions on Bushfire Disaster Education
#### 4.1.2 Sentiments and Attitudes  
When sharing community perceptions through tweets, people also share their emotions. Accordingly, an emotional or sentiment value was derived for each tweet by training the dataset using the Random Tree data classification method using Weka 2.0 software. The data were trained using two-word bags which contained negative and positive words. A sample of words used for data training is given Table 2. Due to the high frequency of words such as 'bushfire' and 'disaster' used in the tweets, they were avoided in preparing the word bags. Using the word bags, all the tweets were categorized under four parameters starting from strongly negative to strongly positive. From the analysed tweets, 18% were strongly negative, 31% were negative, 30% were positive, and 16% were strongly positive. In total, 49% of the tweets were negatively classified and 46% positively classified, whilst 5% were neutral. Figure 6a-d shows the word-cloud analysis for each emotional category. \begin{table}
\begin{tabular}{c c} \hline \hline
**Sentiments** & **Words** \\ \hline Negative & angry, asthma, bad, blame, blood, bother, burn, catastrophic, crisis, cry, curse, danger, dangerous, devastate, die, dizzy, emergency, fear, FFS, fire, flee, frustrate, fuck, haze, horrible, ignore, inadequate, injured, lack, loss, miss, nervous, reckless, red, repugnant, severe, smoke, suffer, tear, terrify, waste, worse, WTF \\ \hline Positive & appreciate, better, bless, brave, confidence, courage, donate, encourage, fine, firefighters, fortunate, fund, happy, help, hug, importance, invaluable, knowledge, lessons, love, modelling, okay, opportunity, plan, praise, prepare, priceless, ready, rescue, research, safe, smile, strong, support, survival, trust, victory \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A sample of word bags for sentiment analysis. Figure 6: (**a**) Negative sentiments; (**b**) Strongly negative sentiments; (**c**) Strongly positive sentiments; (**d**) Positive sentiments. As given in Figure 6a,b, words such as 'helpless','services', 'panic', 'economic','students','minister','services', 'risk', and 'information' were plural among the negatively classified tweets. Further, words such as 'annoy','suffering', 'risk', 'frustrate', and 'preparation' were prominent in the strongly negative word cloud. This reflected the negative emotions people had in relation to the prevailing disaster education and preparation methods. The presence of institutional names such as AWBEC and AAEE in the word cloud for strongly positive tweets (Figure 6c,d) reflected the positive sentiments of the community related to such institutions. Additionally, conferences such as ADRC (Australian Disaster Resilience Conference) 2019 and AFAC and words such as 'engagement','science', and 'consultation' were plural among the positively classified tweets. As [4] mentions, conducting education-centre-based knowledge development is a traditional form of education, and people have seen this service in a positive light. Nonetheless, for an in-depth understanding, the tweets under the four sentiment groups (\(n\) = 13,022) were further classified based on six attitudes (Table 3). Hence, another six attitude words groups were derived after closely observing the word clouds and the tweets of each sentiment category. Fear, anger, and frustration were the main attitudes derived from the negatively classified tweets. Motivation, appreciation, and recommendation were the main attitudes derived from the positively classified tweets. As given in Table 3, another six-word bag set was separately developed to train the dataset again, to classify tweets under the six aforementioned attitude groups using Weka 2.0 software. As given in Figure 7, 11% (\(n\) = 1442) of the total tweets expressed fear with a strong negative emotion. About 6% (\(n\) = 772) of the tweets expressed fear with a negative emotion. Most of the tweets from this category expressed a fear of the prevailing bushfire while requesting more information and evacuation instructions.