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.