Difficulties training a classifier
If I understand correctly, you noticed that a lot of correct predictions were due to a case which always has the same label and is very frequent in your data, right?
Let me say that I think you had a good reasoning: you analyzed the problem, designed a potential solution and then tried it. This kind of problem is very common, but in this case the solution is not really to delete the repeated cases in your training data.
What happens is that you have imbalanced data, that is you have a class which appears very frequently: when you deleted the very frequent instance you mentioned, your data size went from 50000 to 2000, so this instance represents 96% of your data. This instance has probably almost always the same label, so my guess is that this instance with this class as label represents 85% of your data. At first you thought your classifier was working well since you had 85% accuracy, but it's very easy for the classifier to reach 85% of correct predictions if the most frequent class represents 85% of the data: it just needs to always answer this class.
The first lesson here is that accuracy is not a good evaluation measure for imbalanced data: it's too simple, it can be very high even though the classifier doesn't do anything useful. That's why you should probably look at precision, recall and f1-score by class, and typically at macro f1 score as a global evaluation score.
Second point, I'm going to mention resampling because it's usually the standard answer to imbalanced data: the idea is simply to artificially &quot;re-balance&quot; the distribution of the classes in the data by either:

undersampling the frequent class, that is using only a random subset of this class. Note that your idea to delete the frequent instance is actually not so different.
oversampling the other classes, that is repeating the rare instances as many times as necessary to make the data balanced.

Importantly, the resampling must be done only on the training set, otherwise the evaluation on the test set is biased.
Resampling is quite easy, but the results are often disappointing. This is because quite often the problem is deeper: the classifier is just not able to distinguish the classes from the information provided by the features.
That brings me to my final point: the fact that you get 10% accuracy after removing the frequent case shows that the classifier doesn't have a clue what it's doing. Most of the time designing a good ML system needs some serious work with the features, especially with text data. The type of model (and parameters) can also play an important role. You didn't explain what kind of model and what kind of features you use, so there's not much I can say about this. Don't hesitate to post a new question with these details, and maybe we can help you improve this model.