Identifying uninformative words is not an easy task and is domain-dependent. For example, stop words or punctuation often are discriminative a lot for sentiment analysis.
If you want to test the keyness of a words for classification, you may use selection scores as chi2, information gain or mutual information. chi2 score basically assess how good is a score at discriminating two classes based on occurences count.
Thus you could remove the lowest scored words with this technique.
Post-processing can be done as explained by @Toros91, adding that if for instance you consider characters to be garbage you can easily prior remove them based on string length.
Finding the "garbageness" of words is I think not an easy problem and is often over-simplified in text classification.
Don't hesitate if you have further questioning.
EDIT: adding code sample
Using scikit-learn :
from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectPercentile
from sklearn.feature_selection import chi2
iris = load_iris()
X, y = iris.data, iris.target
selector = SelectPercentile(score_func=chi2, percentile=100)
X_reduced = selector.fit_transform(X, y)
indices = numpy.argsort(selector.scores_)
# array containing the indices of features according to ascendant chi2 score
You can then use the indices to remove k worst feature according to chi2 square in your data.