LIWC Dictionary Expansion
•Download as PPTX, PDF•
2 likes•1,957 views
This presentation explains the research I made during while working at the Social Computing Lab at KAIST. The main goal was to expand the LIWC vocabulary and adapt for Twiter sentiment analysis. Download it to see the animations :)
Recommended
More Related Content
Similar to LIWC Dictionary Expansion
Similar to LIWC Dictionary Expansion (20)
Recently uploaded
Recently uploaded (20)
LIWC Dictionary Expansion
- 1. LIWC Dictionary Expansion Luiz Gustavo Ferraz Aoqui Social Computing Lab – GSCT – KAIST
- 2. Motivation • Dictionary-based classifiers have high precision • But usually low recall • Natural language is very dynamic • New words appear • Words change their meaning and sentiment • Heap’s Law • Hard to update the dictionary at the same speed
- 3. LIWC Dictionary • Fairly large dictionary • Almost 4,500 words and steams • 406 positive • 499 negative • Development and Update is a long process • Almost exclusively done manually • Requires a lot of human resources • Last update was in 2007 • Twitter was launched in July, 2006
- 4. System overview 19027743 1985381275 NULL NULL <d>2009-06-01 00:00:00</d> <s>web</s> <t>I think i 'm gonna go with the magic in 6.... just cause now that bron bron's out i wanna see kobe lose too.</t> SeanBennettt 98 434 159 - 18000 0 0 <n>Sean Bennett</n> <u d>2009-01-15 16:36:04</ud> <t>Eastern Time (US & Canada)</t> <l>Long Island, NY</l> . . . Postive: .. :) :- ...... live tweet ;) .& -- =) everytime rain tweets (: mj xd michael !!!!!! lil ." dog sun jus fan wit =] :] aww album via luv photo ;- john pic different kno wearing la ). Negative: !! :( ?? getting twitter omg ?! ppl :/ dude idk da weather bout wtf iphone smh wat internet =( heat dnt =/ facebook :| gosh kate :[ fml ima jon swear punch text =[ cringe ): nd ** imma
- 6. System overview/Parser 19027743 1985381275 NULL NULL <d>2009-06-01 00:00:00</d> <s>web</s> <t>I think i'm gonna go with the magic in 6.... just cause now that bron bron's out i wanna see kobe lose too.</t> SeanBennettt 98 434 159 -18000 0 0 <n>Sean Bennett</n> <ud>2009- 01-15 16:36:04</ud> <t>Eastern Time (US & Canada)</t> <l>Long Island, NY</l> . . . haha nooo! i just wanna kill mee!!!! i didn`t do my homework...and i feel sick =( I can see the bus again. that makes me happy. $$ Black Swan Fund Makes a Big Bet on Inflation wonder how Roubini feels about this...? blahh, i feel boredd and tiredd as hell haha jay to conan... upgrade. lc to kristin... downgrade. rushing home for lauren's final episode. my life makes me sad.
- 7. Parser Structured Extract tweet Tweets Text (RegEx) Filter Clean Tweets Clean Remove Remove Remove user name URL hash tag (RegEx) (RegEx) (RegEx)
- 8. Parser • Regular Expressions • Very powerful tool for text processing… • ..but very complex • Ex.: <d>2009-06-01 00:00:00</d> <s>web</s> <t>I just reached level 2. #spymaster http://bit.ly/playspy</t> asmith393 1522 1498 207 -18000 0 0 I just reached level 2. #spymaster <n>Adam Smith</n> <ud>2007-03-07 <t>(.*?)</t> http://bit.ly/playspy 18:17:20</ud> <t>Eastern Time (US & Canada)</t>
- 9. Parser • Regular Expressions • Very powerful tool for text processing… • ..but very complex • Ex.: I just reached level 2. I just reached level 2. #spymaster #[0-9a-zA-Z+_]* http://bit.ly/playspy http://bit.ly/playspy
- 10. Parser • Regular Expressions • Very powerful tool for text processing… • ..but very complex • Ex.: I just reached level 2. #spymaster ((http://|www.)([a-zA- I just reached level 2. #spymaster http://bit.ly/playspy Z0-9/.~])*)
- 11. System overview/Master haha nooo! i just wanna kill mee!!!! i didn`t do my homework...and i feel sick =( I can see the bus again. that makes me happy. $$ Black Swan Fund Makes a Big Bet on Inflation wonder how Roubini feels about this...? blahh, i feel boredd and tiredd as hell haha jay to conan... upgrade. lc to kristin... downgrade. rushing home for lauren's final episode. my life makes me sad. Index Frequency Chunks Co-frequency
- 12. Master Tweets Splitter Tweets Chunks Mapper Tweets Indexer Index M M M R Reducer R R Unsorted Co-frequency Co-frequency Frequency Sort Co-frequency Frequency
- 13. Master/Splitter • Count the lines in the input file • Select only tweets that words on the LIWC dictionary • Split the input file in smaller chunks
- 14. Master/Indexer • Simply save the vocabulary on a file sorted alphabetically • Important in the future
- 15. Master/Mapper • Spawn processes in parallel and divide the chunks among them • Each worker does two jobs: • First: create (word, frequency) pairs Frequency.tmp someone 6 down 8 ever 10 Chunk Worker kinda 2 crazy 14 …
- 16. Master/Mapper • Spawn processes in parallel and divide the chunks among them • Each worker does two jobs: • First: create (word, frequency) pairs • Second: save the co-words for each word
- 17. Master/Mapper Split Words Remove Duplicates Generate files Save co-words Worker haha haha i nooo do haha nooo! i just wanna kill ! didn`t mee!!!! i didn`t do my i my homework...and i feel sick =( just homework wanna ... and kill mee feel =( i !!!! sick
- 18. Master/Mapper/Issues • Splitting is not trivial • Splitting in whitespaces • homework… ≠ homework • Remove punctuation • :) ☐ • Solution: RegEx again • ([w-'`]*)(W*) • File names: • Unique, easy to find and respect OS rules • Hash • This is why the index file is important
- 19. Master/Mapper/Issues • Parallel programming on Python • Original interpreter don’t support multi-thread… • Alternatives, such as Jython and IronPython, do • …but it is still possible to work in parallel • Multi-thread vs. Multi-process • Multi-process in Python • multiprocessing module • http://docs.python.org/library/multiprocessing.html#module- multiprocessing.pool
- 20. Master/Reducer • Spawn processes in parallel and split the words among them • Basically counts the mapper results • Also, each work does two jobs: • First: sums all the (word, frequency) pairs and save frequency.tmp car 4 frequency.txt house 2 Reducer car 5 ball 5 house 3 car 1 ball 5 house 1
- 21. Master/Reducer • Spawn processes in parallel and split the words among them • Basically counts the mapper results • Also, each work does two jobs: • First: sums all the (word, frequency) pairs and save • Second: sums the co-occurrence frequency trip trip car 1 Worker car 3 ball 3 Ball 3 car 2 house 1 house 1
- 22. Master/Reducer/Issues • Index file • Useful to access the files • Each word has a file with a list of co-words • But file name is hashed • Non-invertible function • Look-up on index, hash the word and get the file
- 23. Master/Sort • Simply sort the frequencies file • Most frequent first
- 24. Classifier α β γ Frequency Scores δ Co-frequency Max results New words
- 25. Classifier/Sentiment words Car 232 Ball 143 Street 125 Top α% Frequency House 121 Boat 114 Pencil 105 Pen 98 Computer 81
- 26. Classifier/Co-words Top β% engine tire door Car Ball court game play Street name size
- 27. Classifier/Score engine tire door engine 1 0 court game play tire 1 0 door 2 1 door size size 1 2 size room type home price size door
- 28. Classifier/Collapse • Created to deal with problems like: • :) :)) :), :). • They should all be treated as the same token • Harder for words
- 29. Classifier/New words • Rules to compare the scores • So far the rules are • If the positive score is bigger than the negative score plus delta, tag the word as positive • Same idea for negative • Returns the new words up to a maximum value
- 30. Other ideas • WordNet based • PMI similarity score
- 31. Evaluation • Two evaluation methods: • First method • Find tweets that could not be categorized before but now they can • Manually check the precision of the result • Second method • Manually select positive and negative tweets • Compare the precision of the old dictionary with the new dictionary
- 32. Sub-product • LIWC Dictionary Library for Python • Provides easy access to the dictionary information • Easy search • Reverse index • Match wildcard • Ex.: