---

Algorithm

Supervised learning algorithm based on the Naïve Bayes theorem
The data set for training and testing is obtained from a popular cocktail recipe website [2] using the Beautiful soup library in python [4]. The extracted data including names of the cocktails, ingredients, rating values and rating counts is stored into the file “recipes”. To classify the data, Naïve Bayes classifier was used, which describes the probability of an event based on prior knowledge of conditions linked to that event's occurrence [1].

Mathematically, it is stated as follows: P(A | B) = ( P(B | A) * P(A) ) / P(B), where A and B are events and P(B) ≠ 0.
P(A | B) is a conditional probability: the likelihood of event A occurring given that B is true.
P(B | A) is also a conditional probability: the likelihood of event B occurring given that A is true.
P(A) and P(B) are the probabilities of observing A and B independently of each other; this is known as the marginal probability.

Sentiment Analysis approach
The positive and negative features are extracted from the two classes [3]. Take the positive class as the example: each recipe in the positive classed has been broken into words to form the positive features [3]. The mean of the rating value can be calculated. Depending on whether the rating of the recipe was higher or lower than the mean, the recipe was classified as positive or negative respectively. Each class was stored in one file. 3/4 of the features were used to train the Naive Bayes Classifier and 1⁄4 of them were used to test the classifier and calculate the accuracy of the algorithm.

Implementation

After training and testing the algorithm, six chosen ingredients were input into the algorithm. Using the itertool library, the possible combinations can be generated. For the initial implementation, we limited the machine’s capability to mix 3 ingredients. After the user selecting the desired cocktail combination, the trained algorithm can determine the likelihood of this combination to be positively appreciated by people.

Check the Video

^{[1] Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine learning, 29(2- 3), 131-163.
[2] 1001 Cocktails. Retrieved December 08, 2018 from 1001cocktails.com
[3] Al-Amrani, Y., Lazaar, M., & Elkadiri, K. E. (2017, March). Sentiment Analysis using supervised classification algorithms. In Proceedings of the 2nd International Conference on Big Data, Cloud and Applications (p. 61). ACM.
[4] Beautiful Soup Library. Retrieved December 08, 2018 from https://www.crummy.com/software/BeautifulSoup/}