How to improve my f1 score in stories analyze
Author gender prediction belongs to the category of stylometry tasks, i.e. related the the style of the text rather than the content. In general this distinction requires a quite different approach than standard content-based tasks, here are a few of the most common differences:

Content words (nouns, adjectives, adverbs) are potentially less important, so TFIDF and similar methods make little sense. It depends on the data though.
Usually it's counter-productive to remove stop words, because they can be actually very useful and distinctive of an author's style (at least their proportion in a text).
For reasons which are not really clear, it's been found experimentally that bigrams and trigrams of characters are often better features than words. This has been observed at least in English, I don't know if this is applicable to other languages.

In general it's not recommended to target a specific level of performance unless it's a baseline known to be reachable, e.g. when another model can do it on similar data. That being said, in the case of this task I would say that 0.7 F1-score is reasonably realistic, but it really depends a lot on the data.
In case you are interested in researching the literature in order to apply state of the art methods, there have been some shared tasks about this question, sometimes mixed with other predictions such as age. search for &quot;author profiling&quot; and &quot;gender profiling&quot; in the PAN Shared Tasks. You might also find other relevant methods in the general category of &quot;author classification/verification&quot;.