word mapping for 2D word embedding - vector

Question

For my Masters Thesis, I created a Word2Vec model. I wanted to show this image to clarify the result. But how does the mapping works to display the words in this 2D space?
All words are represented by a vector of 300 dim. How are they mapped on this 2D image? What are the x & y axes?
Code:
w2v_model.build_vocab(documents)
words = w2v_model.wv.vocab.keys()
vocab_size = len(words)
print("Vocab size", vocab_size)
w2v_model.train(documents, total_examples=len(documents),
epochs=W2V_EPOCH)
tokenizer = Tokenizer()
tokenizer.fit_on_texts(df_train.text)
vocab_size = len(tokenizer.word_index) + 1
print("Total words", vocab_size)
x_train = pad_sequences(tokenizer.texts_to_sequences(df_train.text), maxlen=SEQUENCE_LENGTH)
x_test = pad_sequences(tokenizer.texts_to_sequences(df_test.text), maxlen=SEQUENCE_LENGTH)
labels = df_train.target.unique().tolist()
labels.append(NEUTRAL)
encoder = LabelEncoder()
encoder.fit(df_train.target.tolist())
y_train = encoder.transform(df_train.target.tolist())
y_test = encoder.transform(df_test.target.tolist())
y_train = y_train.reshape(-1,1)
y_test = y_test.reshape(-1,1)
embedding_matrix = np.zeros((vocab_size, W2V_SIZE))
for word, i in tokenizer.word_index.items():
if word in w2v_model.wv:
embedding_matrix[i] = w2v_model.wv[word]
print(embedding_matrix.shape)
embedding_layer = Embedding(vocab_size, W2V_SIZE, weights=[embedding_matrix], input_length=SEQUENCE_LENGTH, trainable=False)

Answer 1

There are a couple of approaches.
The first is to use PCA (principal components analysis), and plot the first component on the x-axis, the second component on the y-axis (and throw away the other components).
You don't say which library you are using to generate your word vectors, and it might come with its own PCA function. But sklearn has one: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
(https://machinelearningmastery.com/develop-word-embeddings-python-gensim/ has some ready-made code showing making the vectors with gensim, then plotting them with that function.)
The other approach you could try is just to plot the first two dimensions of your word vectors. This is reasonable because all dimensions in word vectors should be carrying equal weight. I.e. taking any two of the 300 dimensions should give you the same amount of information as any other two dimensions.
But using PCA is the more normal approach for visualization.