AI in Social Sciences: A New Frontier
The convergence of artificial intelligence and social sciences is a
rapidly growing discipline that represents a paradigm change in how we
comprehend, analyse, and forecast human behavior. AI provides new tools
to social scientists, going beyond traditional qualitative and
quantitative methodologies and allowing for deeper, more complicated
analysis. Currently, AI applications in the social sciences are mostly
focused on data analysis and prediction. Machine learning, a form of
artificial intelligence, has had a particularly significant influence on
several domains. Machine learning algorithms, such as the Long
Short-Term Memory (LSTM) recurrent neural network, have shown promise in
handling massive volumes of textual data and extracting sentiment
patterns from it [16]. LSTM is a specialized type of recurrent
neural network architecture designed to capture long-range dependencies
in sequential data, making it particularly effective for tasks like
sentiment analysis [‘17]. The equation governing the LSTM
architecture involves multiple steps, including the calculation of
input, forget, and output gates, as well as the update of cell states
and hidden states. In summary, the LSTM equation can be represented as
follows:
\begin{equation}
i_{t}=\sigma(W\text{xi}xt+W\text{hi}ht-1+W\text{ci}ct-1+bi)\nonumber \\
\end{equation}\begin{equation}
f_{t}=\sigma(W\text{xf}xt+W\text{hf}ht-1+W\text{cf}ct-1+bf)\nonumber \\
\end{equation}\begin{equation}
\operatorname{=tanh}\left(W\text{xg}xt+W\text{hg}ht-1+W\text{cg}ct-1+bg\right)\nonumber \\
\end{equation}\begin{equation}
o_{t}=\sigma(W_{\text{xo}}x_{t}+W_{\text{ho}}\text{ht}-1+W_{\text{co}}C_{t}+\text{bo})\nonumber \\
\end{equation}\begin{equation}
c_{t}=\text{ft}\odot\text{ct}-1+\text{it}\odot\text{gt}\nonumber \\
\end{equation}\begin{equation}
h_{t}=o_{t}\odot\tanh{(c_{t})}\ \nonumber \\
\end{equation}Where: xt is the input at time step t , ht −1 is
the hidden state at time step t −1, ct −1 is the cell state
at time step t −1, it , ft , gt , ot
are the input, forget, cell, and output gates respectively, W andb are weights and biases, σ is the sigmoid activation
function, ⊙ represents element-wise multiplication.
This equation governs the flow of information through the LSTM network,
allowing it to capture long-term dependencies in sequential data, such
as text. By analysing textual data with LSTM networks, social scientists
can extract nuanced sentiment patterns, providing valuable insights into
human behavior across various domains. Incorporating LSTM networks into
social sciences research enhances the understanding of sentiment
analysis and its applications in fields like psychology, sociology,
economics, and political science [18]. This demonstrates the pivotal
role of AI in advancing social sciences research, particularly in
analysing complex human behaviours and societal trends.