# Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks
## 2 Related Work  
In this section, a review is performed on state-of-art work related to keystroke dynamics analysis for building models for sentiment and stress detection, and the influence of emotion and stress on keystroke dynamics, to assess whether it is suitable to add to our system analyzers that use keystroke dynamics as input to detect sentiment and stress. We will also review works about multi-modal sentiment state detection, where various inputs are used for the detection of the user sentiment, which goes in line with our new MAS. Moreover, to the best of our knowledge, there is no such a system that uses a combination of sentiment and stress analysis from text and keystroke dynamics for discovering whether a message could generate a negative repercussion in a SNS and warns the user in the moment of posting a message. Keystroke dynamics data has been used in the literature to predict the sentiment of the person writing a text. In [11] a group of subjects was asked to type numbers after hearing each of the International Affective Digitized Sounds 2 nd edition (IADS-2) [12] and the keystroke dynamics were recorded. They found evidence through statistical analysis that supports that keystroke duration and latency are influenced by arousal. Keystroke dynamics have been studied for discovering the effect of emotion on keystroke data, but they have also been used to build different emotion detection models. In [8] a group of people was asked to type and label the text typed with their emotional state. Then, classifiers for different emotional states that use keystroke dynamics data as input were successfully built, reaching an accuracy of 77.4% to 87.8% for the confidence, hesitance, nervousness, relaxation, sadness, and tiredness classifiers. In regard to the relation with keystroke dynamics and stress levels, in [13] authors gathered keystroke dynamics data from a group of people in two separate scenarios (with normal conditions and under stress). They discovered that about half the keystroke parameters change significantly (after performing the corresponding t-test) from the data of the scenario of normal conditions to the one wherein the subjects were influenced by stress. Additionally, in [14], authors successfully built different machine learning models that detected cognitive and physical stress using keystroke dynamics features (decision tree, support vector machines, artificial neural networks, k-nearest neighbor, and AdaBoost). Multimodal sentiment analysis has started gaining more attention from researchers recently. There are three main approaches for assessing multimodal sentiment analysis, which are early, intermediate, and late fusion [15]. Early fusion combines different data sources into a single feature vector. As an example of early fusion, in [16] authors extracted features from audio, video, and text, and later fused them with a multiple kernel learning classifier. Intermediate fusion is performed fusing the data in the intermediate layers of the model itself (e.g., in the intermediate layers of an ANN). Finally late fusion is the process of combining the outputs of different sentiment classifiers, trained with different modalities of data for giving a final decision on sentiment classification. In [15], three different models are presented--two unimodal models for sentiment classification, using deep convolutional neural networks (CNN) and image data, and other that employs a long-short term memory network (LSTM) and text data, respectively. The third model combines the output visual features from the CNNs and text features from the LSTM before feeding a fully connected layer with the combination for giving a sentiment classification, which is an example of intermediate fusion. The authors also created a framework for late fusion, where they take into account the outputs of the three models presentedfor giving a final sentiment classification. As has been shown, different strategies with unimodal and multimodal data have been employed in the literature for sentiment analysis.