a machine learning based review system

Are you sure machine learning is the proverbial Silver Bullet?

What should the algorithms do if people enter

"I hate this I hate this I hate this"?

Simply dismiss? Surely you'll have your computer go and dismiss this! I mean, that's not a valid review, or is it!
But wait... What if ...

• What if this person does this all the time? Yet, at the same time, what if this person's taste for some reason resembles the majority of buyers perfectly? How would your algorithm know that "I hate this" couldn't be more accurate for 95% of all buyers?
• What if your reviewers don't want to do a "real good job" writing their review, because... well... they are the customer! They are not your unpaid review slaves, or are they? What if the language includes bitter sarcasm that seems like a very positive experience to your algorithm? Example:

This lovely little game really hits the sweet spots! It never fails (to crash often)! It also makes up for it's cute little content (of 17 whopping minutes) with a lot of really unexpected refreshing moments (every 2nd save game is corrupted). I have no idea why my friends don't play this game, I will definitely (!) recommend it!

Mind you, this sarcastic review might be 100% accurate!
So, maybe you will say "Players probably shouldn't use sarcasm in their reviews!" - Well, I don't want to insult you, but forbidding your customers to use sarcasm is an insult itself in my humble opinion! Also, how will convey what is allowed in a review and what it isn't allowed? Will you write a handbook for anyone who wants to review a game? Do customer reviews require certification so your algorithms can always be sure that any review will abide to your machine's rulesets?

Example: Very cheap and stupid asset flips!

Oh my god, don't you see that pure hatred against that kind of garbage is the most sensible thing to feel about them?

• What if there is nothing else to say about some of the cheapest and meanest asset flips, other than...

I hate this! I hate this! I hate this! I hate this! I hate this! I hate this! I hate this! I hate this! NOT A GAME, NO CONTENT, DON'T BUY!

I mean "I hate this" probably is the best description you could ever imagine for some "games" out there, be they asset flips or even original games!
Don't you agree?

How could your machine learning algorithms make any sense out of such reviews, even if they are perfectly accurate for a human being and sanely warn other players before they buy?

Now imagine you're the customer. You write your review.

• The machine says: "Review accepted. Calculated score: Rated as 'Highly Positive' at 89%. Overall score for this game adjusted from 41% to 74%. Thank you."

Imagine that this just isn't true, you hate the game, but the machine thinks you think otherwise. Will you ever write a review again or will you say "Screw this!"

Now imagine this second case:

• The review machine says: "Your review is not acceptable. Maybe you need to calm down a little? New reviews will no longer accepted for this game by your account. Any reviews by your account will be paused for 4 weeks. I'm just a bot, you cannot appeal. Have a nice day!"
• Ruleset Violations: adult language with spoof factor detected: "Witch" (detected as spoof for "b_tch"), "demands Surrender of the Second-Born" (detected as spoof for "P_rn"). Also: "Magic Runes" detected in database for Nazi symbolism.

Will you ever write a review again if the machine insulted you?

I'll tell you what I would do with your machine learning:

(Assuming that your machine learning will dismiss a lot of comments and reviews as spam or garbage, for example)

1. See how the algorithm works. It probably might insist on good grammar and will dismiss (or misinterpret!) typos. This will immediately dismiss 85% of all reviews written by people who've learned English as a 2nd language - even if all their reviews all are written well-mannered and make sense to any human being because they will clearly see that their grammar might be clumsy, but the review itself can't be considered hostile or invalid.
2. Extract rulesets: Language, length of sentences and needed variations to please and nudge your algorithms.
3. Write my own algorithms that will create perfect reviews that are still 100% fake. If it's cheaper to hire copywriters, I'll do that in the meanwhile.

Related:

• Automated journalism https://en.wikipedia.org/wiki/Automated_journalism#Credibility_and_quality
• https://en.wikipedia.org/wiki/ReCAPTCHA#Criticism
• Researchers Fool ReCAPTCHA With Google’s Own Speech-To-Text Service, motherboard: Are you sure you can do better AI to spot robot reviews than Google can? Or at least create an AI that will come close? Will Steam invest as much money as needed to get to a decent level of bot detection?

TL;DR I don't trust your machine learning algorithms.