This is a complete list of command line options for the Java version of SentiStrength, as of September 2018.

  =Source of data to be  classified=
    text [text to process] OR
    input [filename] (each line of the file is classified SEPARATELY
           May have +ve 1st col., -ve 2nd col.  in evaluation mode) OR
    annotateCol [col # 1..] (classify text in col, result at line end) OR
    textCol, idCol [col # 1..] (classify text in col, result & ID in new  file) OR
    inputFolder  [foldername] (all files in folder will be *annotated*)
    outputFolder [foldername where to put the output (default: folder of  input)]
    resultsExtension [file-extension for output (default _out.txt)]
    statsFile  [filename] (for evaluations, copies eval. stats to file)
     fileSubstring [text] (string must be present in files to annotate)
     Ok to overwrite files [overwrite]
    listen [port number to listen at - call http://127.0.0.1:81/text]
    cmd (wait for stdin input, write to stdout, terminate on input: @end
    stdin (read from stdin input, write to stdout, terminate when stdin  finished)
    wait (just initialise; allow calls to public String  computeSentimentScores)
   =Linguistic data source=
    sentidata [folder for SentiStrength data (end in slash, no spaces)]
   =Options=
    keywords [comma-separated list - sentiment only classified close to  these]
      wordsBeforeKeywords [words to classify before keyword (default 4)]
      wordsAfterKeywords [words to classify after keyword (default 4)]
    trinary (report positive-negative-neutral classifcation instead)
    binary (report positive-negative classifcation instead)
    scale (report single -4 to +4 classifcation instead)
    emotionLookupTable [filename (default: EmotionLookupTable.txt)]
    additionalFile [filename] (domain-specific terms and evaluations)
    lemmaFile [filename] (word tab lemma list for lemmatisation)
   =Classification algorithm parameters=
    noBoosters (ignore sentiment booster words (e.g., very))
    noNegators (don't use negating words (e.g., not) to flip sentiment) -OR-
    noNegatingPositiveFlipsEmotion (don't use negating words to flip +ve  words)
    bgNegatingNegativeNeutralisesEmotion (negating words don't neuter -ve  words)
    negatedWordStrengthMultiplier (strength multiplier when negated  (default=0.5))
    negatingWordsOccurAfterSentiment (negate sentiment occurring before  negatives)
     maxWordsAfterSentimentToNegate (max words sentiment to negator (default  0))
    negatingWordsDontOccurBeforeSentiment (don't negate sentiment after  negatives)
      maxWordsBeforeSentimentToNegate (max from negator to sentiment  (default 0))
    noIdioms (ignore idiom list)
    questionsReduceNeg (-ve sentiment reduced in questions)
    noEmoticons (ignore emoticon list)
    exclamations2 (sentence with ! counts as +2 if otherwise neutral)
    minPunctuationWithExclamation (min punctuation with ! to boost term  sentiment)
    mood [-1,0,1] (default 1: -1 assume neutral emphasis is neg, 1, assume is  pos
    noMultiplePosWords (multiple +ve words don't increase positive sentiment)
    noMultipleNegWords (multiple -ve words don't increase negative sentiment)
    noIgnoreBoosterWordsAfterNegatives (don't ignore boosters after negating  words)
    noDictionary (don't try to correct spellings using the dictionary)
    noMultipleLetters (don't use additional letters in a word to boost  sentiment)
    noDeleteExtraDuplicateLetters (don't delete extra duplicate letters in  words)
    illegalDoubleLettersInWordMiddle [letters never duplicate in word  middles]
       default for English: ahijkquvxyz (specify list without  spaces)
    illegalDoubleLettersAtWordEnd [letters never duplicate at word ends]
       default for English: achijkmnpqruvwxyz (specify list without  spaces)
    sentenceCombineAv (average sentiment strength of terms in each sentence)  OR
    sentenceCombineTot (total the sentiment strength of terms in each  sentence)
    paragraphCombineAv (average sentiment strength of sentences in each text)  OR
    paragraphCombineTot (total the sentiment strength of sentences in each  text)
     *the default for the above 4 options is the maximum, not the total or  average
    negativeMultiplier [negative total strength polarity multiplier, default  1.5]
    capitalsBoostTermSentiment (sentiment words in CAPITALS are stronger)
    alwaysSplitWordsAtApostrophes (e.g., t'aime -> t ' aime)
    MinSentencePosForQuotesIrony [integer] quotes in +ve sentences indicate  irony
    MinSentencePosForPunctuationIrony [integer] +ve ending in !!+ indicates  irony
    MinSentencePosForTermsIrony [integer] irony terms in +ve sent. indicate  irony
    MinSentencePosForAllIrony [integer] all of the above irony terms
    lang [ISO-639 lower-case two-letter langauge code] set processing  language
   =Input and Output=
    explain (explain classification after results)
    echo (echo original text after results [for pipeline processes])
    UTF8 (force all processing to be in UTF-8 format)
    urlencoded (input and output text is URL encoded)
   =Advanced - machine learning [1st input line ignored]=
    termWeights (list terms in badly classified texts; must specify  inputFile)
    optimise [Filename for optimal term strengths (eg.  EmotionLookupTable2.txt)]
    train (evaluate SentiStrength by training term strengths on results in  file)
      all (test all option variations rather than use default)
      numCorrect (optimise by # correct - not total classification  difference)
      iterations [number of 10-fold iterations] (default 1)
      minImprovement [min. accuracy improvement to change sentiment  weights (default 1)]
      multi [# duplicate term strength optimisations to change sentiment  weights (default 1)]
      skipheaderline [ignore the first line of input file (default  true)]
      noheaderline [don't ignore the first line of input file (default  false)]