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Age Group Classification In Social Networks Using Deep Learning

AGE GROUP CLASSIFICATION IN SOCIAL NETWORKS USING DEEP LEARNING

  • Project Code :
  • TCMAPY96
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AGE GROUP CLASSIFICATION IN SOCIAL NETWORKS USING DEEP LEARNING

Age group classification in social networks using deep learning (DL)

Abstract:-

Social networks have a large amount of data available, but often, people do not provide some of their personal data, such as age, gender, and other demographics. Although the sentiment analysis uses such data to develop useful applications in people’s daily lives, there are still failures in this type of analysis, either by the restricted number of words contained in the word dictionaries or because they do not consider the most diverse parameters that can influence the sentiments in a sentence. Thus, more reliable results can be obtained; if the users profile information and their writing characteristics are considered. This research suggests that one of the most relevant parameters contained in the user profile is the age group, showing that there are typical behaviors among users of the same age group, specifically, when these users write about the same topic. Furthermore, in order to validate the usefulness of the proposed model for classifying age groups, it is implemented into the enhanced sentiment metric. In the performance validation, subjective tests are performed and the e-SM, with the proposed model reached a root mean square error and a Pearson correlation coefficient of 0.25 and 0.94, respectively, outperforming the e-SM metric, when the age group information is not available. Deep learning machine algorithms are tested for the classification of the teenager and adult age group, and the deep convolution neural network has given the best performance.

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