To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints.

Let's denote:
- \(x_1\) as the number of premium seats sold,
- \(x_2\) as the number of regular seats sold.

The objective is to maximize profit, with each premium seat generating $40 in profit and each regular seat generating $20. Thus, the objective function can be written as:
\[ \text{Maximize: } 40x_1 + 20x_2 \]

Given constraints are:
1. The total number of seats (premium and regular) cannot exceed 100.
\[ x_1 + x_2 \leq 100 \]
2. At least 10 seats must be premium.
\[ x_1 \geq 10 \]
3. At least 5 times as many people prefer regular seats to premium seats, implying:
\[ x_2 \geq 5x_1 \]

Therefore, the symbolic representation of the problem is:

```json
{
    'sym_variables': [('x1', 'number of premium seats'), ('x2', 'number of regular seats')],
    'objective_function': '40*x1 + 20*x2',
    'constraints': ['x1 + x2 <= 100', 'x1 >= 10', 'x2 >= 5*x1']
}
```

This problem can be solved using linear programming techniques, and Gurobi is a powerful tool for solving such optimization problems. Here's how you could implement this in Python using Gurobi:

```python
from gurobipy import *

# Create a new model
m = Model("Tour_Bus_Profit")

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="premium_seats")
x2 = m.addVar(vtype=GRB.INTEGER, name="regular_seats")

# Set the objective function
m.setObjective(40*x1 + 20*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 100, "total_seats")
m.addConstr(x1 >= 10, "min_premium_seats")
m.addConstr(x2 >= 5*x1, "regular_vs_premium")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: Premium seats = {x1.x}, Regular seats = {x2.x}")
    print(f"Maximum Profit: ${40*x1.x + 20*x2.x}")
else:
    print("No optimal solution found")
```