We have the data loaded and can see its structure. It includes various metrics like mean values, standard deviations, and frequencies for different words. To analyze this visually, we can look into distributions of specific metrics, compare values across categories, or track changes in sentiment scores for words. Let's start by visualizing some key aspects of this data. What would you like to focus on first? For example, we could explore the distribution of mean values for happiness, anger, or any other emotion across all words.

To continue with the analysis, we'll visualize the distribution of mean happiness (mean_hap) and mean anger (mean_ang) scores across all words. This will give us an insight into the overall emotional tone of the words in the dataset.

We'll create histograms for both mean happiness and mean anger scores to see their distribution.

We've visualized the distribution of mean happiness and mean anger scores across all words in the dataset using histograms. These visualizations help us understand the emotional tone distribution among the words. The histograms showcase how the scores are spread out, indicating the variety of emotional content in the dataset.

Next, we'll compare the mean fear (mean_fear) and mean sadness (mean_sad) scores across all words to further understand the dataset's emotional landscape. We'll create scatter plots to observe any potential relationships between these emotions.

To test the null hypothesis that higher levels of fear lead to higher levels of sadness, we'll perform a statistical test. Specifically, we can use a Pearson correlation test to evaluate the strength and direction of the relationship between mean fear and mean sadness scores. This will help us determine if the observed correlation is statistically significant.