I am working on a social network analysis project. My data comes from twitter. Before I run the analysis, I intend to apply clustering- specifically Kmeans to determine how to seperate tweets in categories. I vectorized my data using the following code: vectorizer3 = TfidfVectorizer(stop_words = stop_words, tokenizer = tokenize, max_features = 1000) X3=vectorizer3.fit_transform(df_connections['text'].values.astype('str')) word_features3 = vectorizer3.get_feature_names_out() len(word_features3) Next, I run the following code: from sklearn.cluster import KMeans clusters = [2, 3, 4, 5, 10, 15] for i in clusters …

I've been working on a NN-based classification system that accepts document vectors as input. I can't really talk about what I'm specifically training the neural net on, so i'm hoping for a more general answer. Up to now, the word vectors I've been using (specifically, the gloVe function from the text2vec package for R) have been target vectors. Up to now I wasn't aware that the word2vec training produced context vectors, and quite frankly I'm not sure what exactly they …

The W2V techniques define context as a window of k words around the term, and using this learn the vector representations for words in the corpus. Attention networks can help us get the important information from a sequence. I was wondering, can attention networks help define context better, which I can then use for learning the word embeddings? I wasn't able to find any article/paper that learns word embeddings like this. They first learn the vectors using word2vec methods and …

Suppose I have a corpus with documents: corpus = [ "The sky looks lovely today", "The fat cat hit the poor dog", "God created all men equal", "He wrestled the creature to the ground", "The king did not treat his subjects fairly", ] Which I've preprocessed, and want to generate context-word pairs, following this article. The writer notes: The preceding output should give you some more perspective of how X forms our context words and we are trying to predict …