4 ms·

Side note: possibly for a project/homework involving online reputation management. While in college, we were also tasked with doing something very similar for a

Side note: possibly for a project/homework involving online reputation management. While in college, we were also tasked with doing something very similar for an international competition. Given this and the overall tone of the request for 'help', I'd caution everyone familiar with NLP to be wary of actually giving a fully coded solution.

blauwbilgorgel 13y ago
Good catch. I'll just post my results. I classified it with a simple algo[1] using the normalized frequency distributions of unigrams and bigrams in the 50-sample training set. Company ( 0.0567236272499 ) iPhone = Eye Phone = Illuminati Phone. Siri spelled backwards is Iris, thats a part of the Eye. Apple is Illuminati. They're watching you. Company ( 0.208253968254 ) Apple caught testing offline Dictation for iOS 7 http://idb.tc/1d2GKph Company ( 0.0578323858427 ) RT @jonnyevans_cw: Why #Apple really, really doesn't need a shopkeeper to lead its retail chain http://shar.es/AhsUK via @computerworld Company ( 0.0242924384131 ) The Best Music Streaming Apps For Your iPhone #Apple #iPhone http://bit.ly/14xwinU Company ( 0.0249022556391 ) Apple May Be Working on Self-Adjusting Noise-Cancelling Headphones http://on.mash.to/12hFDMu via @mashable Company ( 0.488585099111 ) Samsung Continues Ad Campaign against Apple's iPhone in Iceland Company ( 0.179605263158 ) the creator of the iPhone Company ( 0.0764348527178 ) I Used To Hate Apple, And Now I'm A Giant Sell-Out http://bit.ly/IGL0h5 #archivesWeek in Review | YSL Chief Executive to Apple, Bec Astley Clarke, Fashion Sweatshirts, Esteban Cortazar http://bit.ly/128MPf5 via @BoFI've been single since Apple was just a fruit. Fruit ( 0.727605245395 ) I be up so high trying to get a piece of that apple pie Fruit ( 0.438529121875 ) I want to eat healthy I really do. But I just found a whole apple pie in my fridge. Fruit ( 0.662778904665 ) Apple banana and a cup of milo Fruit ( 0.0214817448669 ) Apple I look like a human heart. Mango I look like a stomach. Grapes I look like eyes. Banana I don't like this game. Fruit ( 0.0815735543081 ) An apple potato and onion all taste the same if you eat them with your nose plugged. Fruit ( 0.229706852 ) PSYCHOLOGY Test Choose 1 among the fruits below: APPLE MANGO GRAPES PEAR BANANA Fruit ( 0.00714560473592 ) Today's shake is spinach, avocado, apple orange banana blueberry with Chia seed and protein feelin the energy Fruit ( 0.238171611868 ) Banana Bread topped with Apple Maple Syrup and Yoghurt Fruit ( 0.184395490353 ) Mid-PM Snack Apple blueberries frozen mixed berries plain @Alpro_UK soya yoghurt, coconut flakes & agave nectar pic.twitter.com/4188sHosA5 Fruit ( 0.0788802216228 ) Green spinach kale apple banana garlic ginger Orange lemon ginger cayenne #juicing http://instagram.com/p/bbT6L3yZyU/ [1] Using libraries for this like http://nltk.org/_modules/nltk/classify/naivebayes.html http://nltk.org/_modules/nltk/classify/naivebayes.html or http://scikit-learn.org/stable/modules/svm.html http://scikit-learn.org/stable/modules/svm.html would probably be more accurate and faster to implement. Also I didn't cross-validate on the training set, but used new samples.
matiasb 13y ago
I did some experiments with Ruby and Wikipedia data, first I implemented an #ambiguous? method for articles (to check if the article links to an disambiguation page). The second idea was to implement a method to disambiguate a given sentece or paragraph by using term frequency and idf from wikipedia articles: disambiguation pages provide a set of categories and articles, a corpus is built up from articles of each category. So for a given text you may infer its category. https://github.com/matiasinsaurralde/wikipedia https://github.com/matiasinsaurralde/wikipedia

blauwbilgorgel 13y ago

Good catch. I'll just post my results. I classified it with a simple algo[1] using the normalized frequency distributions of unigrams and bigrams in the 50-sample training set. Company ( 0.0567236272499 ) iPhone = Eye Phone = Illuminati Phone. Siri spelled backwards is Iris, thats a part of the Eye. Apple is Illuminati. They're watching you. Company ( 0.208253968254 ) Apple caught testing offline Dictation for iOS 7 http://idb.tc/1d2GKph Company ( 0.0578323858427 ) RT @jonnyevans_cw: Why #Apple really, really doesn't need a shopkeeper to lead its retail chain http://shar.es/AhsUK via @computerworld Company ( 0.0242924384131 ) The Best Music Streaming Apps For Your iPhone #Apple #iPhone http://bit.ly/14xwinU Company ( 0.0249022556391 ) Apple May Be Working on Self-Adjusting Noise-Cancelling Headphones http://on.mash.to/12hFDMu via @mashable Company ( 0.488585099111 ) Samsung Continues Ad Campaign against Apple's iPhone in Iceland Company ( 0.179605263158 ) the creator of the iPhone Company ( 0.0764348527178 ) I Used To Hate Apple, And Now I'm A Giant Sell-Out http://bit.ly/IGL0h5 #archivesWeek in Review | YSL Chief Executive to Apple, Bec Astley Clarke, Fashion Sweatshirts, Esteban Cortazar http://bit.ly/128MPf5 via @BoFI've been single since Apple was just a fruit. Fruit ( 0.727605245395 ) I be up so high trying to get a piece of that apple pie Fruit ( 0.438529121875 ) I want to eat healthy I really do. But I just found a whole apple pie in my fridge. Fruit ( 0.662778904665 ) Apple banana and a cup of milo Fruit ( 0.0214817448669 ) Apple I look like a human heart. Mango I look like a stomach. Grapes I look like eyes. Banana I don't like this game. Fruit ( 0.0815735543081 ) An apple potato and onion all taste the same if you eat them with your nose plugged. Fruit ( 0.229706852 ) PSYCHOLOGY Test Choose 1 among the fruits below: APPLE MANGO GRAPES PEAR BANANA Fruit ( 0.00714560473592 ) Today's shake is spinach, avocado, apple orange banana blueberry with Chia seed and protein feelin the energy Fruit ( 0.238171611868 ) Banana Bread topped with Apple Maple Syrup and Yoghurt Fruit ( 0.184395490353 ) Mid-PM Snack Apple blueberries frozen mixed berries plain @Alpro_UK soya yoghurt, coconut flakes & agave nectar pic.twitter.com/4188sHosA5 Fruit ( 0.0788802216228 ) Green spinach kale apple banana garlic ginger Orange lemon ginger cayenne #juicing http://instagram.com/p/bbT6L3yZyU/ [1] Using libraries for this like http://nltk.org/_modules/nltk/classify/naivebayes.html http://nltk.org/_modules/nltk/classify/naivebayes.html or http://scikit-learn.org/stable/modules/svm.html http://scikit-learn.org/stable/modules/svm.html would probably be more accurate and faster to implement. Also I didn't cross-validate on the training set, but used new samples.

matiasb 13y ago

I did some experiments with Ruby and Wikipedia data, first I implemented an #ambiguous? method for articles (to check if the article links to an disambiguation page). The second idea was to implement a method to disambiguate a given sentece or paragraph by using term frequency and idf from wikipedia articles: disambiguation pages provide a set of categories and articles, a corpus is built up from articles of each category. So for a given text you may infer its category. https://github.com/matiasinsaurralde/wikipedia https://github.com/matiasinsaurralde/wikipedia