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    <p>If all you want is adjective frequencies, then the problem is relatively simple, as opposed to some brutal, not-so-good machine learning solution.</p> <p><strong>Wat do?</strong></p> <p>Do POS tagging on your text. This annotates your text with part of speech tags, so you'll have 95% accuracy or more on that. You can tag your text using the <a href="http://nlp.stanford.edu:8080/parser/" rel="nofollow noreferrer">Stanford Parser online</a> to get a feel for it. The parser actually also gives you the grammatical structure, but you only care about the tagging.</p> <p>You also want to make sure the sentences are broken up properly. For this you need a sentence breaker. That's included with software like the Stanford parser.</p> <p>Then just break up the sentences, tag them, and count all things with the tag ADJ or whatever tag they use. If the tags don't make sense, look up the Penn Treebank tagset (Treebanks are used to train NLP tools, and the Penn Treebank tags are the common ones).</p> <p><strong>How?</strong></p> <p>Java or Python is the language of NLP tools. Python, use NLTK. It's easy, well documented and well understood.</p> <p>For Java, you have GATE, LingPipe and the Stanford Parser among others. It's a complete pain in the ass to use the Stanford Parser, fortunately I've suffered so you do not have to if you choose to go that route. See my <a href="https://sites.google.com/site/nicoflacco/" rel="nofollow noreferrer">google page</a> for some code (at the bottom of the page) examples with the Stanford Parser.</p> <p><strong>Das all?</strong></p> <p>Nah, you might want to stem the adjectives too- that's where you get the root form of a word:</p> <p><em>cars -> car</em></p> <p>I can't actually think of a situation where this is necessary with adjectives, but it might happen. When you look at your output it'll be apparent if you need to do this. A POS tagger/parser/etc will get you your stemmed words (also called lemmas).</p> <p><strong>More NLP Explanations</strong> See <a href="https://stackoverflow.com/questions/6854455/someone-can-give-a-simple-explanation-about-the-elements-of-natural-language-pro">this question</a>.</p> <p><img src="https://i.stack.imgur.com/vtV9h.jpg" alt="enter image description here"></p>
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    1. COWow, thanks so much that is all very helpful.
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    2. COIf you only want parts-of-speech (like is it an adjective?), it'll be much faster to use just a part-of-speech tagger (like the Stanford POS Tagger) rather than a full parser. Full parses would help for getting negation scope right, but you may well be able to handle that heuristically - look for a negation in a few preceding words before encountering a punctuation mark.
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    3. COSorry, I should have mad that distinction (tagger vs parser vs what each software package contains)
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