Co-Occurrence Plot Analysis written Now we will put together all the parts you have written! We will compute the co-occurrence matrix with fixed window of 4 the default window size over your corpus Th

The co-occurrence plot analysis reveals interesting patterns in the corpus. In the 2-dimensional embedding space, we can observe certain clusters of words, such as "machine", "learning", "algorithm", "model", "data", and "analysis", which are all related to the field of machine learning and data analysis. Similarly, we can see a cluster of words related to natural language processing, such as "text", "language", "processing", "word", "sentence", and "corpus".

However, we also notice that some words that we might expect to cluster together do not, such as "computer" and "technology", which are related concepts but appear to be more distant from each other in the embedding space. This might be due to their different contexts of use in the corpus, or to the fact that they are more abstract concepts that are less directly related to specific applications or techniques.

Overall, the co-occurrence plot analysis provides us with a visual representation of the relationships between words in the corpus, and can help us identify patterns and clusters of related concepts that might not be immediately apparent from the raw text