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When you don't have label/categories then it's called Unsupervised Learning. You can solve this problem via Latent Dirichlet Allocation (LDA) model and then evaluate your model by splitting the texts in half and compare the topic assignment for each half using cosine similarity. The more similar the topic assignment, the better. Example


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Maybe you don't have a positive and a negative class. Your input are word vectors. Unless you trained your word vectors before with explicit positive and negative labels, it is very unlikely that your KMeans learned that difference. If you used pre-trained word vectors, your KMeans could have learned an arbitrary difference between cluster 0 and cluster 1. ...


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NLTK has a sentiment module. You can try and check the statistics of positive vs negative for each text in the clusters.


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A Text embedding is a vector representation of a text. A trivial way to construct a text embedding is to average the word embeddings of each word in the text. However using this method, you will lose contextual information.


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