Features of word vectors in Word2Vec

I am trying to do sentiment analysis. In order to convert the words to word vectors, I am using Word2Vec model. Suppose I have all the sentences in a list named 'sentences' and I am passing these sentences to word2vec as follows:

model = word2vec.Word2Vec(sentences, workers=4 , min_count=40, size=300,   window=5, sample=1e-3)


Since I am noob to word vectors, I have two doubts:

1- Setting the number of features to 300 defines the features of a word vector. But what these features signify? If each word in this model is represented by a 1x300 numpy array, then what do these 300 features signify for that word?

2- What does downsampling as represented by 'sample' parameter in the above model do in actual?

These features are learnt by the neural network as this is unsupervised method. Each vector has several set of semantic characteristics. For instance, let's take the classical example, V(King) -V(man) + V(Women) ~ V(Queen) and each word represented by 300-d vector. V(King) will have semantic characteristics of Royality, kingdom, masculinity, human in the vector in a certain order. V(man) will have masculinity, human, work in a certain order. Thus when V(King)-V(Man) is done, masculinity,human characteristics will get nullified and when added with V(Women) which having femininity, human characteristics will be added thus resulting in a vector much similar to V(Queen). The interesting thing is, these characteristics are encoded in the vector in a certain order so that numerical computations such as addition, subtraction works perfectly. This is due to the nature of unsupervised learning method in neural network.
2- There are two approximation algorithms. Hierarchical softmax and negative sampling. When the sample parameter is given, it takes negative sampling. In case of hierarchical softmax, for each word vector its context words are given positive outputs and all other words in vocabulary are given negative outputs. The issue of time complexity is resolved by negative sampling. As in negative sampling, rather than the whole vocabulary, only a sampled part of vocabulary is given negative outputs and the vectors are trained which is so much faster than former method.