diff --git a/Haggressive.cabal b/Haggressive.cabal new file mode 100644 --- /dev/null +++ b/Haggressive.cabal @@ -0,0 +1,51 @@ +name: Haggressive +version: 0.1.0.0 +synopsis: Aggression analysis for Tweets on Twitter +description: Aggression analysis for Tweets on Twitter +homepage: http://github.io/pold87/Haggressive +license: GPL-2 +license-file: LICENSE +author: Volker Strobel +maintainer: volker.strobel87@gmail.com +category: Web +build-type: Simple + +cabal-version: >=1.10 + +library + hs-source-dirs: src-lib + build-depends: base >=4.7 && <5, + vector, + containers, + Cabal, + HUnit, + text, + cassava, + tuple, + bytestring, + directory, + tokenize, + PSQueue + exposed-modules: Hag, Preprocess, Tweets, Helpers, Evaluation + default-language: Haskell2010 + ghc-options: -fllvm + +executable Haggressive + main-is: Main.hs + build-depends: base, Haggressive, Cabal, HUnit + hs-source-dirs: src-exe + default-language: Haskell2010 + ghc-options: -fllvm + +test-suite tests + type: detailed-0.9 + test-module: My + hs-source-dirs: testsuite/tests + build-depends: base + , Haggressive + , HUnit + , containers + , vector + , Cabal + , tuple + default-language: Haskell2010 diff --git a/LICENSE b/LICENSE new file mode 100644 --- /dev/null +++ b/LICENSE @@ -0,0 +1,339 @@ + GNU GENERAL PUBLIC LICENSE + Version 2, June 1991 + + Copyright (C) 1989, 1991 Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. 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If this is what you want to do, use the GNU Lesser General +Public License instead of this License. diff --git a/Setup.hs b/Setup.hs new file mode 100644 --- /dev/null +++ b/Setup.hs @@ -0,0 +1,2 @@ +import Distribution.Simple +main = defaultMain diff --git a/src-exe/Main.hs b/src-exe/Main.hs new file mode 100644 --- /dev/null +++ b/src-exe/Main.hs @@ -0,0 +1,8 @@ +module Main where + +import qualified Evaluation +import qualified Hag + +main :: IO () +main = + Hag.main diff --git a/src-lib/Evaluation.hs b/src-lib/Evaluation.hs new file mode 100644 --- /dev/null +++ b/src-lib/Evaluation.hs @@ -0,0 +1,79 @@ +module Evaluation + (Evaluation.main) + where + +import Control.Monad +import qualified Data.ByteString.Char8 as BS +import qualified Data.ByteString.Lazy as L +import qualified Data.ByteString.Lazy.Char8 as BSL +import Data.Char +import Data.Csv +import Data.Either +import Data.List +import qualified Data.Map as M +import Data.Ord +import qualified Data.PSQueue as PS +import qualified Data.Text as T +import Data.Text.Encoding +import qualified Data.Text.IO as TI +import qualified Data.Vector as V +import Hag +import Helpers +import NLP.Tokenize +import Preprocess +import Preprocess +import qualified System.Directory as S +import System.Environment +import Tweets + + +-- |Create a dictionary ('M.Map String Float' /TODO/: probably define +-- dictionary type) from a 'V.Vector' of 'Tweet's +createDictionary :: V.Vector Tweet -> FeatureMap +createDictionary tweets = V.foldl (M.unionWith (+)) M.empty allTweets + where allTweets = V.map extractFeatures tweets + +-- |Create a 'grand dictionary' ('M.Map String Float' /TODO/: probably +-- define dictionary type) from a 'mini dictionary' ('M.Map Tweet +-- (M.Map String Float)' +createDictionaryFromMap :: M.Map Tweet FeatureMap -> FeatureMap +createDictionaryFromMap tweetMap = foldl (M.unionWith (+)) M.empty $ M.elems tweetMap + +main :: IO () +main = do + (dir:_) <- getArgs + + files <- getFiles dir + csvs <- mapM TI.readFile $ sort files + let processedCsvs = map preprocess csvs + r = map parseCsv processedCsvs + listOfVectorOfTweets = rights r :: [V.Vector Tweet] + -- Tweet vectors + vTweets = V.concat listOfVectorOfTweets :: V.Vector Tweet + aggTweets = filterByLabel vTweets "aggressive" :: V.Vector Tweet + nonAggTweets = filterByLabel vTweets "non_aggressive" :: V.Vector Tweet + + -- Tweet dictionaries + dict = createDictionary vTweets + aggDict = createDictionary aggTweets + nonAggDict = createDictionary nonAggTweets + + words = (encode $ sortBy (comparing $ snd) $ + M.toList dict) :: L.ByteString + + aggWords = (encode $ sortBy (comparing $ snd) $ + M.toList aggDict) :: L.ByteString + + nonAggWords = (encode $ sortBy (comparing $ snd) $ + M.toList nonAggDict) :: L.ByteString + + -- Create a header + header = encode ["word","frequency"] + + -- Write output to csv + L.writeFile "agg_words.csv" $ header `L.append` aggWords + L.writeFile "non_agg_words.csv" $ header `L.append` nonAggWords + L.writeFile "words.csv" $ header `L.append` words + print $ V.length aggTweets + + diff --git a/src-lib/Hag.hs b/src-lib/Hag.hs new file mode 100644 --- /dev/null +++ b/src-lib/Hag.hs @@ -0,0 +1,232 @@ +{-# OPTIONS_GHC -Wall #-} +{-# LANGUAGE OverloadedStrings #-} + + +{-| +Module : Hag +Description : Classify Tweets (aggressive vs. non_aggressive) and evaluate + classification performance. +License : None +Maintainer : Volker Strobel (volker.strobel87@gmail.com) +Stability : experimental +Portability : None + +This module is the main interface for Tweet classification. + +-} +module Hag +( module Hag + ) where + +import qualified Data.ByteString.Lazy as L +import Data.Char +import Data.Csv +import Data.Either +import Data.List +import qualified Data.Map as M +import qualified Data.PSQueue as PS +import qualified Data.Text as T +import Data.Text.Encoding +import qualified Data.Text.IO as TI +import qualified Data.Vector as V +import Helpers +import NLP.Tokenize +import Preprocess +import qualified System.Directory as S +import System.Environment +import Tweets + + +-- | Features are represented by a 'M.Map', where the keys are +-- 'String's (e.g., the words in the message of a Tweet) and the +-- values are 'Float's (e.g., the number of occurrence of a word) +type FeatureMap = M.Map String Float + +-- | +-- =IO and Parsing + +-- |'parseCsv' parses a 'T.Text' input for fields in CSV format and +-- returns a 'Vector' of 'Tweet's +parseCsv :: T.Text -> Either String (V.Vector Tweet) +parseCsv text = decodeWith + defaultDecodeOptions {decDelimiter = fromIntegral $ ord '\t' } + NoHeader + (L.fromStrict $ + encodeUtf8 text) + +-- |Get directory contents of 'FilePath'. A better variant is at: +getFiles :: FilePath -> IO [FilePath] +getFiles dir = S.getDirectoryContents dir + >>= return . map (dir ++) . filter (`notElem` [".", ".."]) + +-- |Extract features (the bag of unigrams) for one Tweet. +-- Thereby, the Tweet will be (in order of application): +-- * tokenized +-- * converted to a 'V.Vector' +-- * 'String's will be converted to lowercase +-- * 'String's that are element of 'stopWords' are removed +-- * Empty 'String's will be removed +extractFeatures :: Tweet -> FeatureMap +extractFeatures tweet = bagOfUnigrams + where + pre = V.filter (`notElem` ["USER", "RT"]) + . V.fromList + . tokenize + . tMessage + bagOfUnigrams = frequency + . V.filter (/= "") + . V.filter (`notElem` stopWords) + . V.map (map toLower) + . pre + $ tweet + +-- |Calculate the 'frequency' of items in a 'V.Vector' and return them +-- in a 'M.Map'. +frequency :: V.Vector String -> FeatureMap +frequency = V.foldl' countItem M.empty + +-- |Insert an item into a 'M.Map'. Default value is 1 if the item is +-- not existing. If the item is already existing, its frequency will +-- be increased by 1. +countItem :: M.Map String Float -> String -> FeatureMap +countItem myMap item = M.insertWith (+) item 1 myMap + +-- |Take a 'M.Map', consisting of +-- key: 'Tweet' +-- value: 'FeatureMap' +-- and one Tweet and create a new 'M.Map' with the added features +-- from the 'Tweet' +insertInMap :: M.Map Tweet FeatureMap + -> Tweet + -> M.Map Tweet FeatureMap +insertInMap oldMap tweet = M.insert tweet val oldMap + where val = extractFeatures tweet + +-- | Compare two vectors of 'Tweet's, the first is the test vector, +-- the second the train vector and return the all neighbors for each +-- 'Tweet'. 'grandDict' is a 'M.Map', where each entry consits of a +-- 'Tweet' and its features +getNeighbors :: (V.Vector Tweet, V.Vector Tweet) + -> V.Vector (Tweet, [PS.Binding Tweet Float]) +getNeighbors (v1,v2) = V.map (featureIntersection dictionary) v1 + where dictionary = V.foldl insertInMap M.empty v2 :: M.Map Tweet FeatureMap + +-- | Take a dictionary and a 'Tweet' and return a pair of this 'Tweet' +-- and all its nearest neighbors +featureIntersection :: M.Map Tweet FeatureMap + -> Tweet + -> (Tweet, [PS.Binding Tweet Float]) +featureIntersection tweetMap tweet = (tweet, mini) + where + mini = PS.fromList + $ M.elems + $ M.mapWithKey (mergeTweetFeatures cosineDistance tweet) tweetMap + +-- |Take a distance function, 'Tweet' 1, 'Tweet' 2 and a dictionary as +-- 'FeatureMap' and create a 'PS.Binding' between 'Tweet' 2 and the +-- distance from this 'Tweet' to the other 'Tweet'. +mergeTweetFeatures :: (FeatureMap -> FeatureMap -> Float) + -> Tweet + -> Tweet + -> FeatureMap + -> PS.Binding Tweet Float + +mergeTweetFeatures distF t1 t2 _ = queue + where featuresT1 = extractFeatures t1 + featuresT2 = extractFeatures t2 + distance = distF featuresT1 featuresT2 + queue = t2 PS.:-> distance + +-- |Take the features of two 'Tweet's and return the distance as +-- 'Num'. +cosineDistance :: FeatureMap -> FeatureMap -> Float +cosineDistance t1 t2 = negate (mySum / (wordsInT1 * wordsInT2)) + where + wordsInT1 = M.foldl (+) 0 t1 + wordsInT2 = M.foldl (+) 0 t2 + intersection = M.elems $ M.intersectionWith (*) t1 t2 + mySum = foldl (+) 0 intersection + +-- |Takes a dictionary and a mini dictionary (frequency of words in +-- one Tweet) and calculates the idftf values for all words in the +-- mini dictionary. +idftf :: FeatureMap -> FeatureMap -> FeatureMap +idftf grandDict miniDict = M.mapWithKey (iFrequency grandDict) miniDict + +iFrequency :: FeatureMap -> String -> Float -> Float +iFrequency dict word freq = freq * (log (totalNumberOfWords / freqWord)) + where freqWord = M.findWithDefault 1 word dict + totalNumberOfWords = M.foldl (+) 0 dict + +-- | Calculate the amount of tweets where the predicted label matches +-- the actual label. +compareLabels :: Int -> V.Vector (Tweet,[PS.Binding Tweet Float]) -> V.Vector Float +compareLabels k vec = V.map + (\(a,b) -> if (tLabel a) == getLabel k b then 1 else 0) + vec + +compareLabelsForScheme :: [V.Vector (Tweet,[PS.Binding Tweet Float])] -> Int -> [Float] +compareLabelsForScheme vecs k = map (getAccuracy . compareLabels k) vecs + + +-- | Get the label for a 'Tweet' by looking at the k nearest +-- neighbors. If there are more aggressive than non_aggressive +-- 'Tweet's, the label will be aggressive, otherwise, it will be +-- non-aggressive. +getLabel :: Int -> [PS.Binding Tweet Float] -> String +getLabel k queue = if agg >= nonAgg then "aggressive" else "non_aggressive" + where -- tweets = queueTake k queue + tweets = take k queue + labels = map (tLabel . PS.key) tweets + agg = length $ filter (== "aggressive") labels + nonAgg = length $ filter (== "non_aggressive") labels + +-- | Get sum total of a vector of floats (i.e., the number of +-- correctly classified tweets) and return the accuracy +getAccuracy :: V.Vector Float -> Float +getAccuracy vec = (V.foldl (+) 0 vec) / fromIntegral (V.length vec) + +main :: IO () +main = do + -- Retrieve command line args + (dir:_) <- getArgs + + -- Get list of files in directory dir + files <- getFiles dir + + -- Read content of all files into list, one Text per item + csvs <- mapM TI.readFile $ sort files + + let + -- Add quotation marks for CSV parsing + processedCsvs = map preprocess csvs + + -- Create Tweets from Text + r = map parseCsv processedCsvs + + -- If parsing was successful, extract Right elements from the + -- Either list + tweets = rights r + + -- Create a leave-one-out cross-validation scheme + scheme = mkCrossValScheme tweets + + -- Get all neighbors for all tweets for all schemes + allNeighbors = map getNeighbors scheme + + -- k should be in {1..100} + ks = [1..100] + + -- Compare all training tweets to test tweets for all ks + comparedTweets = map (compareLabelsForScheme allNeighbors) ks + + -- Prepare for CSV + results = encode comparedTweets + + -- Create header + -- header = encode + -- $ map (("fold_" ++) . show) ([1..10] :: [Integer]) + + -- Write output to a file + -- L.writeFile "resultsK.csv" $ header `L.append` results + L.writeFile "resultsK.csv" $ results diff --git a/src-lib/Helpers.hs b/src-lib/Helpers.hs new file mode 100644 --- /dev/null +++ b/src-lib/Helpers.hs @@ -0,0 +1,41 @@ +module Helpers + (mkCrossValScheme + , queueTake + , stopWords) + where + +import qualified Data.PSQueue as PS +import qualified Data.Vector as V +import Debug.Trace +import System.IO.Unsafe + +-- | 'FilePath' of the 'stopWords' that are removed before the +-- dictionary is created +stopWordsFile :: FilePath +stopWordsFile = "dutch-stop-words.txt" + +-- | Read the 'stopWords' from the 'stopWordsFile' +stopWords :: [String] +stopWords = lines file + where file = unsafePerformIO $ readFile stopWordsFile + +-- | Make a cross-validation scheme from a list of vectors +mkCrossValScheme :: (Eq a) => [V.Vector a] -> [(V.Vector a,V.Vector a)] +mkCrossValScheme xs = map (leaveOneOut xs) xs + +-- | Create pair of a list of vectors and a vector that specifies +-- which vector should be left out +leaveOneOut :: (Eq a) => [V.Vector a] -> V.Vector a -> (V.Vector a,V.Vector a) +leaveOneOut all test = (test, V.concat $ filter (/= test) all) + +-- | Take the first k elements of a queue +queueTake :: (Ord k, Ord p, Show k) => Int -> PS.PSQ k p -> [k] +queueTake k queue = queueTake' k queue [] + +-- | Helper function for 'queueTake' +queueTake' :: (Ord k, Ord p) => Int -> PS.PSQ k p -> [k] -> [k] +queueTake' 0 _ acc = acc +queueTake' k queue acc = case mini of + Nothing -> [] + Just m -> queueTake' (k - 1) (PS.deleteMin queue) (PS.key m:acc) + where mini = PS.findMin queue diff --git a/src-lib/Preprocess.hs b/src-lib/Preprocess.hs new file mode 100644 --- /dev/null +++ b/src-lib/Preprocess.hs @@ -0,0 +1,19 @@ +{-# OPTIONS_GHC -Wall #-} +{-# LANGUAGE OverloadedStrings #-} + +module Preprocess + (preprocess) + where + +import qualified Data.Text as T + +preprocess :: T.Text -> T.Text +preprocess txt = T.cons '\"' $ T.snoc escaped '\"' + where escaped = T.concatMap escaper txt + +escaper :: Char -> T.Text +escaper c + | c == '\t' = "\"\t\"" + | c == '\n' = "\"\n\"" + | c == '\"' = "\"\"" + | otherwise = T.singleton c diff --git a/src-lib/Tweets.hs b/src-lib/Tweets.hs new file mode 100644 --- /dev/null +++ b/src-lib/Tweets.hs @@ -0,0 +1,36 @@ +module Tweets + (filterByLabel +, Tweet(..)) where + +import Control.Applicative ((<$>), (<*>), (<|>)) +import Control.Monad (mzero) +import Data.Csv +import qualified Data.PSQueue as PS +import qualified Data.Vector as V + +-- | Parsing Record to Tweet +instance FromRecord Tweet where + parseRecord v + | V.length v == 5 = Tweet <$> + v.! 0 <*> + v.! 1 <*> + v.! 2 <*> + v.! 3 <*> + v.! 4 + | otherwise = mzero + +-- | A Tweet consists of a category, a user, a date, a time, and a +-- message +data Tweet = Tweet { tLabel :: String + , tUser :: String + , tDate :: String + , tTime :: String + , tMessage :: String + } deriving (Show, Eq, Ord) + + +-- | Filter 'Tweet's by label +filterByLabel :: V.Vector Tweet -> String -> V.Vector Tweet +filterByLabel tweets label = V.filter (\t -> tLabel t == label) tweets + + diff --git a/testsuite/tests/My.hs b/testsuite/tests/My.hs new file mode 100644 --- /dev/null +++ b/testsuite/tests/My.hs @@ -0,0 +1,162 @@ +module My where + +import Data.Map as M +import Data.Tuple.Curry +import Data.Vector as V +import qualified Distribution.TestSuite as TS +import qualified Hag as H +import qualified Test.HUnit as HU + + + +-- Little helpers + +-- parseTweet(s) were in Hag but it turned out that they are not necessary +-- there, since you can parse the CSV ad hoc, i.e., during reading the file + +parseTweets :: V.Vector (String, String, String, String, String) -> V.Vector H.Tweet +parseTweets = V.map $ uncurryN H.Tweet + +parseTweet :: (String, String, String, String, String) -> H.Tweet +parseTweet (a,b,c,d,e) = H.Tweet a b c d e + + +-- Lists +list1, list2, list3 :: [String] +list1 = [ "ich", "bin", "aber", "ich", "bin", "nicht" ] +list2 = ["hey", "du", "hey", "du"] +list3 = [] + +-- Vectors +vec1, vec2, vec3 :: V.Vector String +vec1 = V.fromList list1 +vec2 = V.fromList list2 +vec3 = V.fromList list3 + + +-- Maps +map1, map2 :: M.Map String Int +map1 = M.fromList [ ("ich", 2) + , ("bin", 2) + , ("aber", 1) + , ("nicht", 1) + ] +map2 = M.fromList [ ("hab", 2) + , ("bin", 2) + , ("aber", 1) + , ("nicht", 2) + ] + +-- Tuples +tup1 = ("aggressive", "Monday", "Time", "User", "Whatup?? I like this sh******t!!") +tup2 = ("a", "b", "c", "d", "e") +tup3 = ("foo", "baa", "haa", "mu", "sa") + +tup4 = ("1", "2", "3", "4", "5") +tup5 = ("z", "y", "x", "a", "Oh my goodness \"!ยง$%&/()=?") + +tup6 = ("b", "b", "b", "b", "Text Mining is cool") +tup7 = ("a", "a", "a", "a", "Text Mining sucks") + +-- Tweets +[tweet1, tweet2, tweet3, tweet4, tweet5, tweet6, tweet7] = Prelude.map parseTweet [tup1, tup2, tup3, tup4, tup5, tup6, tup7] + +-- Define tests + +-- frequency -- +testFrequency1, testFrequency3 :: HU.Test +testFrequency1 = H.frequency vec1 HU.~?= map1 +testFrequency2 = H.frequency vec2 HU.~?= map2 +testFrequency3 = H.frequency vec3 HU.~?= M.empty + + + +vecTweets1, vecTweets2, vecTweets3 :: V.Vector H.Tweet +vecTweets1 = parseTweets $ V.fromList [tup1, tup2, tup3] +vecTweets2 = parseTweets $ V.fromList [tup4, tup5] +vecTweets3 = parseTweets $ V.fromList [tup2, tup5] + + + +tweet1_m :: H.Tweet +tweet1_m = H.Tweet "aggressive" + "Monday" + "Time" + "User" + "Whatup?? I like this sh******t!!" + + + + +-- parseTweet +parseTweetTest1 :: HU.Test +parseTweetTest1 = parseTweet tup1 HU.~?= tweet1_m + + +mkCrossValSchemeTest0, mkCrossValSchemeTest1, mkCrossValSchemeTest2 :: HU.Test +mkCrossValSchemeTest0= H.mkCrossValScheme ([] :: [V.Vector H.Tweet]) HU.~?= [] +mkCrossValSchemeTest1 = + H.mkCrossValScheme [vecTweets1, vecTweets2] HU.~?= [(vecTweets1, vecTweets2) + , (vecTweets2, vecTweets1) + ] +mkCrossValSchemeTest2 = + H.mkCrossValScheme [vecTweets1, vecTweets2, vecTweets3] HU.~?= [(vecTweets1, vecTweets2 V.++ vecTweets3) + , (vecTweets2, vecTweets1 V.++ vecTweets3) + , (vecTweets3, vecTweets1 V.++ vecTweets2)] + + + +featureIntersectionTest1 :: HU.Test +featureIntersectionTest1 = H.featureIntersection tweet6 (V.fromList [tweet6]) HU.~?= + V.fromList [M.fromList [("text", 2) + , ("mining", 2) + , ("is", 2) + , ("cool", 2)]] + +featureDistanceTest1 :: HU.Test +featureDistanceTest1 = H.featureDistance tweet6 (V.fromList [tweet6, tweet7]) HU.~?= + V.fromList [4, 2] + + + +createDictionaryTest1 :: HU.Test +createDictionaryTest1 = H.createDictionary (V.fromList [tweet6, tweet7]) HU.~?= + M.fromList [("text", 2) + , ("mining", 2) + , ("is", 1) + , ("sucks", 1) + , ("cool", 1)] + +hunitTests = HU.TestList [testFrequency1 + , testFrequency3 + -- , parseTweetTest1 + , mkCrossValSchemeTest0 + , mkCrossValSchemeTest1 + , mkCrossValSchemeTest2 + , createDictionaryTest1 + , featureIntersectionTest1 + , featureDistanceTest1 + ] + +runHUnitTests :: HU.Test -> IO TS.Progress +runHUnitTests tests = do + (HU.Counts cases tried errors failures) <- HU.runTestTT tests + return $ if errors > 0 + then TS.Finished $ TS.Error "There were errors in the HUnit tests" + else if failures > 0 + then TS.Finished $ TS.Fail "There were failures in the HUnit tests" + else TS.Finished TS.Pass + +tests :: IO [TS.Test] +tests = return [ TS.Test hunit ] + where + hunit = TS.TestInstance + { TS.run = runHUnitTests hunitTests + , TS.name = "HUnit Test Cases" + , TS.tags = ["hunit"] + , TS.options = [] + , TS.setOption = \_ _ -> Right hunit + } + +main :: IO [TS.Test] +main = tests