Lakhasly

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.

The wide range of applications of sentiment analysis has fostered its evolution. Sentiment analysis
techniques have enabled to make sense of big social media data to make more informed decisions and
understand social events, product marketings or political events. 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.
In the first work, titled “Using Social Media to Identify Consumers’ Sentiments towards Attributes
of Health Insurance during Enrollment Season” [12], van den Broek-Altenburg and Atherly aim at
understanding the consumers’ sentiments towards health insurances. For this purpose, they mined
Twitter discussions and analyzed them using a dictionary-based approach using the NRC Emotion
Lexicon [13], which provides for each word its polarity as well as its related emotion (anger, anticipation,
disgust, fear, joy, sadness, surprise and trust). The main finding of this study is that consumers are
worried about providers networks, prescription drug benefits and political preferences. In addition,
consumers trust medical providers but fear unexpected events. These results suggest that more
research is needed to understand the origin of the sentiments that drive consumers so that insurers can
provide better insurance plans.
In the second contribution, titled “Gender Classification Using Sentiment Analysis and Deep Learning
in a Health Web Forum” [14], Park and Woo deal also with the application of sentiment analysis
techniques to health-related topics. In particular, they apply sentiment analysis for identifying
gender in health forums based on Deep Learning techniques. The authors analyze messages from
an AIDS-related bulletin board fromHealthBoard.com and evaluate both traditional and Deep Learning
techniques for gender classification.
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”, Gutiérrez-Esparza et al. deal with the detection of cyberagression. They build and label
a corpus of cyberagression news from Facebook in Latinamerica and develop a classification model
based on Machine Learning techniques. The developed corpus can foster research in this field, given
the scarcity of lexical resources in languages different from English.