## 2 Previous Works
### Sentiment Analysis and Mental Health
#### 2.3.2 Depressive Moods of Users Portrayed in Twitter  
Ref. [34] extracted 37 sentiment categories using the tool LIWC and examined how the variables were correlated with a user Center for Epidemiological Studies-Depression (CES-D) score. CES-D is a survey that asks the frequency of depression-related symptoms that the patient has suffered over the past week. The final score ranges from 0 to 60, with higher scores indicating more severe depression symptoms [34]. A total of 18 sentiment predictors were found out to be reasonably correlated with the topic. The predictor, example words for each factor, and the coefficients of a multiple regression model for the CES-D score are detailed in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **F1** & **AUC** \\ \hline Negative mood & 0.84 & 0.95 \\ Neutral mood & 0.59 & 0.79 \\ Positive mood & 0.75 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 1: F1 and AUC measures for the use of multimodal features and logistic regression.  
These features were used on a regression model to predict the CES-D scores of users showing a high correlation between causation and friends words and depression. According to the coefficients, we can see that the more relevant predictors are anger, causation, tentative, communication, and friends, increasing or decreasing the CES-D score by 5.04, 12.1, \(-\)6.99, \(-\)3.54, and 15.1 respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Variable** \\ \hline  & Positive \\ \cline{2-3} Emotional variables & Negative \\ \cline{2-3} Sad \\ \cline{2-3}  & Anger \\ \cline{2-3} Anxiety \\ \hline  & Present focus \\ \cline{2-3} Temporal Categories & Past focus \\ \cline{2-3}  & Future focus \\ \hline  & Articles \\ \cline{2-3}  & Prepositions \\ \cline{2-3}  & Auxiliary verb \\ \cline{2-3}  & Adverbs \\ \cline{2-3} Standard Linguistic Dimensions & Conjunctions \\ \cline{2-3}  & Pronouns \\ \cline{2-3}  & Verbs \\ \cline{2-3}  & Negations \\ \hline \hline \end{tabular}
\end{table}
Table 2: Features used for the model that classifies comments as depressive indicative or not [18].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predictor** & **Example words** & **Estimate** & \(p\)**-Value** \\ \hline Positive feeling & Love, nice, good & 3.38 & 0.25 \\ Anxiety & Worried, fearful, nervous & 4.02 & 0.45 \\ Anger & Hate, kill, annoyed & 5.04 & \(<\)0.05 \\ Sadness & Crying, grief, sad & 6.33 & 0.20 \\ Causation & Because, effect, hence & 12.1 & \(<\)0.05 \\ Inhibition & Block, constraint, stop & \(-\)4.29 & 0.11 \\ Tentative & Maybe, perhaps, guess & \(-\)6.99 & \(<\)0.05 \\ Face & Keep up appearances & 5.63 & 0.14 \\ Communication & Tell, speak, claim & \(-\)3.54 & \(<\)0.05 \\ Social reference & He, she, who & 4.98 & 0.12 \\ Friends & Buddy, friend, neighbor & 15.1 & \(<\)0.01 \\ Family & Aunt, mother, daughter & 6.17 & 0.19 \\ Humans & Adult, baby, boy & 2.17 & 0.55 \\ School & School, student, class & 0.68 & 0.79 \\ Work & Job, majors, xerox & \(-\)4.79 & 0.16 \\ Achievement & Earn, hero, win & \(-\)6.47 & 0.087 \\ Sleep and dream & Sleep, nap, dream & \(-\)0.98 & 0.66 \\ Death & Bury, coffin, kill & \(-\)11.6 & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of regression models for predicting CES-D scores. Table extracted from [34].