The wide range of applications of sentiment analysis has fostered its evolution.In the third approach [15], titled "Personality or Value: A Comparative Study of Psychographic
Segmentation Based on an Online Review Enhanced Recommender System", Liu et al. analyze the predictive
and explanatory capability of psychographic characteristics in e-commerce user preferences. For this
purpose, they construct a pychographic lexicon based on seed words provided by psycholinguistics that
are expanded using synonyms from WordNet [16], resulting in positive and negative lexicons for two
psychographic models, Schwartz Value Survey (SVS) [17] and Big Five Factor (BFF) [18]. Then they
construct word embeddings using Word2Vec [9] and extend the corpus with word embeddings
from an Amazon corpus [19]. Finally, they incorporate the lexicons in a deep neural network-based
recommender system to predict the users' online purchasing behaviour. They also evaluate customer
segmentation based on BDSCAN clustering [20], but this does not provide a significant improvement. The main insight of this research is that psychographic variables improve the explanatory power of
e-consumer preferences, but their prediction capability is not significant. Finally, in the fourth work [21], titled "Classification of Cyber-Aggression Cases Applying Machine
Learning", Gutierrez-Esparza et al. deal with the detection of cyberagression.Four works selected in this Special
Issue deal with the application of sentiment analysis for improving health insurances, understanding
AIDS patients, e-commerce user profiling and cyberagression detection.The authors analyze messages from
an AIDS-related bulletin board fromHealthBoard.com and evaluate both traditional and Deep Learning
techniques for gender classification.They build and label
a corpus of cyberagression news from Facebook in Latinamerica and develop a classification model
based on Machine Learning techniques.In particular, they apply sentiment analysis for identifying
gender in health forums based on Deep Learning techniques.