## 4 Results  
At the first stage of data preprocessing from 28 million tweets, around 31,438 tweets were extracted about maternal healthcare from the USA. After the stop word removal and lemmatization, the processed tweets were transferred to the next step, topic modeling. From the LDA topic evaluation that uses different values of \"number of topics\", we find the optimal number of topics to be three. The LDA model coherence values are shown below in Figure 3. In this figure, we can see that for the number of topics 3, the model generated the maximum coherence score of 0.3466. After that, the coherence score decreases as the number of topics increases. Hence, the number of topics for the LDA model was set to three and are visualized in different ways. At first, a word cloud was drawn for every topic depicted in Figure 4. The keywords inside these word clouds represent each topic. Therefore, the frequency of the most represented keywords in each topic is depicted in the following bar charts in Figure 5. It is evident that in Topic 1, \"fever, baby, mother, birth\" are the most frequent. In contrast, Topic 2 has \"healthcare, high, help, worker, public, health medical\" most frequently. In Topic 3, \"coronavirus, positive, flu, birth, pregnant, test\" are the most frequent. Figure 4: Word cloud of three topics. Figure 3: Coherence score of LDA model for different numbers of topics. From the above analysis, labels can be assigned to every topic. Depending on the keyword frequency in Topic 1, users talking about pregnant mothers and newborn baby's mothers are worried about fever; in Topic 2, the impact on healthcare facilities and workers and consequences on expectant mothers; and in Topic 3, the tweets are concerned about the rising flu, which could be coronavirus-positive cases, COVID-19 testing, the rapidly spreading virus, and its effect on pregnant mothers. Depending on these keyword frequencies, every tweet is classified into different topics, shown in three colors in Figure 6. Here, orange, green, and blue tweets represent the three topics based on their keywords. Blue represents Topic 1, orange corresponds to Topic 2, and green represents Topic 3. Next, the distribution of each topic in the dataset is shown in Figure 7. Here, it is evident that most of the tweets fall under Topic 2 and Topic 1. Figure 5: Word count and importance of topic keywords for each topic. Figure 6: Tweets classified into three topics. Furthermore, the three topics were again visualized using t-SNE, shown in Figure 9. Each dot represents a tweet, and the three colors indicate different topics. In the t-SNE graph, the n-dimensional data are described in two-dimensional space. It is clear from the figure that there is little overlap between topics. After classifying the tweets, the sentiment behind each tweet was measured concerning polarity and subjectivity, as in Figure 4.0 The value of polarity ranges from \(-1\) to \(1\). A value of \(-1\) represents a highly negative experience, 1 represents a good experience, and 0 represents a neutral experience. For subjectivity, the value ranged from 0 to 1. If a tweet contains a more significant amount of personal opinion, the subjectivity value would be close to 1. The value would be close to 0 if it contains factual information. Figure 4.1 Inter-topic distance map [120; 121]. Figure 4.2 Distribution of three topics. In Table 3 below, the mean polarity and subjectivity values are calculated. The mean polarity of Topics 1 and 3 conveys negative emotion, and the other topic has fairly positive emotion. However, as the negative and positive polarity tweets negated each other, each topic must be studied separately according to sentiments. First, sorting the dataset for only Topic 1 with negative polarity, which essentially means negative sentiment, and running an N-gram analysis presents the following results, as shown in Figure 4.3 The N-gram analysis shows that mothers worry about their babies catching a fever and how pregnant mothers are severely affected by COVID-19.