I think you want to find words in sentence that are similar to any of the words that represent any of the 6 basic given emotions. If I am correct I think you can use following solution.
First extract synset of each of the word sense representing 6 basic emotions. Now form the vectorized representation of each of these synset(collection of synonymous words). You can do this using word2Vec tool available at https://code.google.com/archive/p/word2vec/ . e.g.
Suppose "happy" has the word senses a1, a2, a3 as its synonymous words then
1. First train Word2Vec tool on any large English Corpus e.g. Bojar corpus
2. Then using trained word2Vec obtain word embeddings(vectorized representation) of each synonymous word a1, a2, a3.
3. Then vectorized representation of synset of "happy" would be average of vectorized representation of a1, a2, a3.
4. In this way you can have vectorized representation synset of each of the 6 basic emotion.
Now for given sentence find vectorized representation of each of the word in using trained word2vec generated vocabulary. Now you can use cosine similarity
(https://en.wikipedia.org/wiki/Cosine_similarity) to find distance(similarity) of each of the word from synset of 6 basic emotions. In this way you can determine emotion(basic level) of the sentence.
Source of the technique : Research paper "Unsupervised Most Frequent Sense Detection using Word Embeddings" by Sudha et. al.(http://www.aclweb.org/anthology/N15-1132)