<p>More negative examples means more model-adjustment is happening, with each 'target' word training-example. So with more work being done, it's <em>plausible</em> that the model will improve somewhat for some purposes. </p>

<p>But those extra calculations require more training time - so the value of increasing that parameter could be weighed against other choices, that also may offer improvement-at-the-cost-of-training-time. For example, increasing <code>window</code> or the number of training-iterations over the corpus also plausibly improve the model at cost of time.</p>

<p>Interestingly, more negative examples tend to bias the coordinate-positions of most words, meaning the "cloud" of vectors isn't centered on the origin point. And, at least one recent paper has suggested a final step of removing this bias – transforming the final coordinates to restore a global average at the origin – can improve the word-vectors' utility on some tasks. </p>

<p>Additionally, the original Word2Vec paper notes that with large corpuses, fewer negative examples may be sufficient or optimal. Section 2.2 of <a href="https://arxiv.org/abs/1310.4546" rel="nofollow noreferrer">'Distributed Representations of Words and Phrases and their Compositionality'</a> notes, "Our experiments indicate that values of k in the range 5–20 are useful for small training datasets, while for large datasets the k can be as small as 2–5." (I've even seen acceptable results, in a large corpus, with a single negative example.) </p>

<p>So, it's worthwhile to experiment with different <code>negative</code> values, and some reasons to believe more examples can help, but it's not automatically a case of "more are better", and especially with larger corpuses, fewer negative examples may be sufficient or even optimal. </p>
How the negative affect model performance in gensim?
<p>I am reading the paper</p>

<p>Distributed Representations of Words and Phrases and their Compositionality.</p>

<p>It is very interesting but I am really curious the relationship between the parameter 'negative' and the final performance. I personally think the final performance may become better as the increase of negative until some value. Because the more negative samples, which we are using to make the comparison, we should get better results theoretical. Of course, the performance will not become better until some points. Am I right?</p>