Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
There is huge amount of data in social networks, where people
post their opinion on a topic, or share their information. But
people often don’t provide their personal data, like gender, age
and other demographics. Research can be done on this data to
develop applications of sentiment analysis, but the success rate
is restricted by the number of words in the dictionaries as they
do not consider all the words which reflect the sentiment in our
messages as most of the communication on social networks is
non-standard language with small messages. Moreover, with
contemporary technology it is quite easy to create profile with
false age, gender and location which provides criminals an easy
way to deceive. Thus we can analyze the text messages posted
by the user on social network platform. As per the research
done so far, age is one of the important parameter in the user
profile which reveals the important information about the
typical behavior among same age group users. An analysis is
done with more than 4000 tuples which contains relevant
parameters like number of friends, length of message, number
of likes, number of hash tags and comments are considered for
the classification. In this study, we use the user profile
information for the prediction of age group, which we collected
using Facebook API. In this paper we classified the users into
two age groups teenagers and adults using different Machine
learning algorithms like deep convolutional neural networks,
Multilayer perceptron, Random forest , SVM and Decision trees.
Among all these algorithms deep convolutional neural network
stands out to be the best among all of them reaching the best
performance with an accuracy of 94%.
