Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

[1]  Rongrong Ji,et al.  A cross-media public sentiment analysis system for microblog , 2014, Multimedia Systems.

[2]  Yau-Hwang Kuo,et al.  Integrated microblog sentiment analysis from users’ social interaction patterns and textual opinions , 2015, Applied Intelligence.

[3]  L. Brody.  Gender and Emotion: Beyond Stereotypes , 2010 .

[4]  T. Davis.  Gender differences in masking negative emotions: Ability or motivation? , 1995 .

[5]  Xin Zhang,et al.  Exploring sentiment parsing of microblogging texts for opinion polling on chinese public figures , 2016, Applied Intelligence.

[6]  Andrew McCallum,et al.  Using Maximum Entropy for Text Classification , 1999 .

[7]  S. Stoner,et al.  Age and Gender Differences with the Anger Expression Scale , 1987 .

[8]  Chia-Hui Chang,et al.  Sentiment-oriented contextual advertising , 2009, Knowledge and Information Systems.

[9]  Erik Cambria,et al.  Affective Computing and Sentiment Analysis , 2016, IEEE Intelligent Systems.

[10]  Byung-Gyu Kim,et al.  Efficient Facial Expression Recognition Algorithm Based on Hierarchical Deep Neural Network Structure , 2019, IEEE Access.

[11]  Ali Selamat,et al.  Hybrid sentiment classification on twitter aspect-based sentiment analysis , 2018, Applied Intelligence.

[12]  Partha Pratim Roy,et al.  Age and gender classification using brain–computer interface , 2019, Neural Computing and Applications.

[13]  Harry Zhang,et al.  The Optimality of Naive Bayes , 2004, FLAIRS.

[14]  F. DeMicco,et al.  Age- and Gender-Based Market Segmentation , 2002 .

[15]  Corinna E. Löckenhoff,et al.  Age differences in descriptions of emotional experiences in oneself and others. , 2008, The journals of gerontology. Series B, Psychological sciences and social sciences.

[16]  A. Kring,et al.  Sex differences in emotion: expression, experience, and physiology. , 1998, Journal of personality and social psychology.

[17]  K. Fingerman,et al.  Age and gender differences in adults' descriptions of emotional reactions to interpersonal problems. , 2003, The journals of gerontology. Series B, Psychological sciences and social sciences.

[18]  Debi Prosad Dogra,et al.  deepGesture: Deep learning-based gesture recognition scheme using motion sensors , 2018, Displays.

[19]  Pragya Keshari,et al.  Effect of Age and Gender on Consumer Response to Advertising Appeals , 2016 .

[20]  Partha Pratim Roy,et al.  Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction , 2019, Inf. Fusion.

[21]  P. Deepa Shenoy,et al.  Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier , 2016, World Wide Web.

[22]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

[23]  Mahir Arzoky,et al.  Deep convolutional neural network designed for age assessment based on orthopantomography data , 2019, Neural Computing and Applications.

[24]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[25]  Fei Liu,et al.  Sentiment analysis based on clustering: a framework in improving accuracy and recognizing neutral opinions , 2013, Applied Intelligence.

[26]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[27]  Peter Zimmermann,et al.  Emotion regulation from early adolescence to emerging adulthood and middle adulthood , 2014 .

[28]  Takaya Saito,et al.  The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets , 2015, PloS one.

[29]  Richard A. Fabes,et al.  Gender and Age Stereotypes of Emotionality , 1991 .

[30]  Vishal. A. Kharde,et al.  Sentiment Analysis of Twitter Data : A Survey of Techniques , 2016, ArXiv.

[31]  Debi Prosad Dogra,et al.  Kinect sensor-based interaction monitoring system using the BLSTM neural network in healthcare , 2018, Int. J. Mach. Learn. Cybern..

[32]  Yung-Ming Li,et al.  Deriving market intelligence from microblogs , 2013, Decis. Support Syst..

[33]  Hamido Fujita,et al.  Successes and challenges in developing a hybrid approach to sentiment analysis , 2017, Applied Intelligence.

[34]  Xiaohua Hu,et al.  Cross-lingual sentiment classification with stacked autoencoders , 2015, Knowledge and Information Systems.

[35]  Patrick Paroubek,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2010, LREC.

[36]  Yong-Gi Kim,et al.  Type-2 fuzzy ontology-based opinion mining and information extraction: A proposal to automate the hotel reservation system , 2015, Applied Intelligence.