Ho

Homer – A Text-Analyzer in Python

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Homer – A Text-Analyzer in Python

Homer is a Python package that can help make your text more clear, simple and useful for the reader. It provides information on an overall text as well as on individual paragraphs. It gives insights into readability, length of paragraphs, length of sentences, average sentences per paragraph, average words in a sentence, etc. It is based on work by Pinker and anohter research study [1, 2]. This software package grew out of a personal need. Since I am not a native English speaker but am interested in writing. I write OpEds and a blog. So I designed and have been using Homer to improve my writing. I hope others will find it useful. Please note that this is not a strict guide to control your writing. At least, I don't use it that way. I use it as a guide to make my writing as simple as possible. I strive to write concise paragraphs and sentences as well as use fewer unclear words, and Homer has been helping me. I have only used it to analyze my blogs and essays and not the large corpus of text. As this software is new, you may well spot bugs, in which case please feel free to open up issues/pull-requests. You can use Homer as a stand-alone package or on the command line. More information: https://github.com/wyounas/homer References: 1- Steven Pinker's The Sense of Style: 2- https://litlab.stanford.edu/LiteraryLabPamphlet9.pdf

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, open · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, way, para · Missing: mobile apps, ios, entrepreneurs
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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