# A White-Box Sociolinguistic Model for Gender Detection
## 2 Automatic Gender Detection: From Discriminant Analysis to Deep Learning
### Fourth Stage  
The latest developments in Machine Learning have been incorporated in recent years in automatic gender detection research. More specifically, Deep Learning structures have been designed to perform gender detection from textual and visual data.  
In [32], [PERSON], [PERSON] and [PERSON] analyzed 56 blogs collected from the SogniLucidi website using a Feed-Forward Neural Network. They reported 77.6% classification accuracy using word unigrams, bigrams, and trigrams as features.  
[PERSON] and [PERSON] [33] explored an AIDS-related bulletin board from HealthBoard.com and created a gender detection model based on the emotions expressed by the users in their comments and posts. They applied the NRC and BING dictionary to extract sentiment information. The results exhibited that Deep-Learning structures, and more specifically, Convolutional Neural Networks, outperformed traditional Machine-Learning algorithms. In fact, they reached 73.44% accuracy with Random Forest, whereas a Convolutional Neural Network structure yielded 91% accuracy.  
In 2020, [PERSON] et al. [34] explored the Enron e-mail corpus with an Artificial Neural Network and the Whale Optimization Algorithm to find the optimal weights and improve the accuracy of the neural network structure. They outperformed previous work on the Enron corpus achieving an accuracy of 98% with 48 linguistic features distributed into four categories: character-based features, word-based features, syntax-based features, and structure-based features.  
Finally, [PERSON] et al. [35] employed Deep Neural Networks and Convolutional Neural Networks on the PAN-AP-17 dataset collected from Twitter, reporting an accuracy of 86.33% using TF-IDF scores and GloVe word vectors.  
Deep Learning methods have been implemented in other data modalities. To mention some examples, refs. [36; 37] employed Convolutional Neural Network (CNN) for gender detection from facial images, and [38] used Multi-Scale Convolutional Neural Networks on audio data.  
Table 1 presents an overview on some of the previous works on automatic gender detection. For each of the works reported, the table shows the dataset, features, and algorithm used as well as the accuracy reached.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Year** & **Dataset** & **Features** & **Algorithm** & **Acc.** \\ \hline [PERSON] & 2001 & Oral conversations & Lexical richness measures & Discriminant analysis & 90.0 \\ \hline [PERSON] \& [PERSON] & 2005 & Telephone conversations & Word unigrams and bigrams & SVM & 92.5 \\ \hline [PERSON] \& [PERSON] & 2006 & Blogs & Dictionary-based and POS n-grams & SVM & 92.5 \\ \hline [PERSON], [PERSON] \& [PERSON] & 2009 & Blogs & Non-dictionary words and sentence length & Naïve Bayes & 89.3 \\ \hline [PERSON] & 2010 & Movie reviews & Lexical frequencies and POS tags & Logistic Regression & 73.3 \\ \hline [PERSON] et al. & 2010 & Twitter & Tokens unigrams and bigrams & SVM & 72.3 \\ \hline [PERSON], [PERSON] \& [PERSON] & 2017 & Twitter & Character n-grams (2, 7) & Sequential Minimal Optimization & 72.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Previous work on automatic gender detection.