## 6 Discussion  
In the previous section, we outlined and briefly discussed the results from a variety of different feature and model-selection approaches. While experiments were conducted on several different algorithms, including deep-learning and ensemble methods, experimentation on linear regression models was more prevalent for the sake of highlighting the effectiveness of features, while also offering better interpretability comparability with the baseline approach. In the end, the set of features included MBTI/Enneagram prediction, a set of _n_-grams stemming from the work of [PERSON] et al. [7] and a set of LIWC-based features created through method described in Section 5.1.4. While the other features that were experimented with led to limited or no improvement at all for certain personality traits, they offered valuable insight into the nature of personality traits. When considering the experiments involving the conversion of Big Five personality prediction into a classification issue rather than a regression one, followed by using these predictions as features in a regression model, we observed that, except for _Openness_, none of the other personality traits saw improvement in the results (shown in Tables 7 and 8). Although it is possible that these improvements for the _Openness_ trait can be attributed to data following a negatively skewed distribution (Figure 6), we suggest that this is a result of the classification predictions for _Openness_ being more statistically independent from other 4 personality traits. Despite the previous research reporting promising results when using binning strategies [76], we propose that the possibility of information loss largely outweighs the positives of this approach [114; 115] when predicting personality traits in this manner. Another set of features that we experimented with was Reddit participation and the way in which this reflects on personality. As this particular set of features demonstrated overall little to no improvement, this suggests that personality has little effect on topical interests and the way they are expressed through Reddit (Table 16). However, as the majority of the most popular subreddits were those that centre around broader topics, it is possible that grouping certain interests into larger classes (e.g., hobbies, music-related and news), and using them as features could lead to a higher correlation with certain personality traits. Due to the breadth of the issue and the overall experimental complexity such study would warrant, we decided to leave it for future works. The LIWC-based features introduced in our experiments led to improvements in predicting Big Five traits; however, they also introduced additionally complexity in the feature space. To best handle this and bring the most out of these features, an adequate regression model was necessary. Algorithms that achieved the best results on each of the Big Five traits did so when they used LIWC features selected through the methodology described in Section 5.1.4. The trait that saw the highest increase was _Openness_, correlating by 3.3% more with the actual trait values than with the baseline approach. These results were achieved when using combination of Elastic-Net model as predictor and a feature set consisting of _n_-grams, MBTI/Enneagram predictions and a set of LIWC features selected for _S-N_--an MBTI type with which _Openness_ demonstrated a statistically significant correlation in the past. The same model yielded the best results for _Conscientiousness_ when the LIWC features were selected for the _J-P_ type, and _Extroversion_ when LIWC features that correlated with for the _E-I_ MBTI type were used. In the case of these traits, the increase was 1.2% for _Conscientiousness_ and 2.1% for _Extroversion_ with a final correlation of 40.8% for the _Extroversion_ trait being the highest correlation value for an individual trait.