paper published on the 6th ICOICT 2018 http://www.icoict.org

1. DYNAMICLARGESCALEDATAONTWITTERUSINGSENTIMENTANALYSISANDTOPIC
MODELING
CaseStudy:Uber
AndryAlamsyah,Wirawan Rizkika, DityaDwiAdhiNugroho
Farhan Renaldi,Siti Saadah
Lab. SocialComputingand BigData
School of Economics and Business, Telkom University
School of Computing, Telkom University
Bandung, Indonesia

2. Presentation
OUTLINE
1. ResearchObjectOverview
2. ResearchBackground
3. ProblemStatement
4. ResearchQuestions& Objectives
5. ResearchFramework
6. ResearchMethodology
7. ResearchResultsAnalysis
8. Conclusion& Suggestion

3. ResearchObjectOverview
83countriesandover674cities
worldwide
OnlineTransportationPioneer
Itisthebestexampleofaneconomysectordisruptingcompany,essentiallyitisonamissiontoreplaceataxiasold-schoolpublictransportationglobally.
Availablein:
$68Million
Valuation(Aprill,2017):

4. ResearchBackground
*HootSuite&WeAreSocial
328.000.000ActiveUser
SocialMediaPlatformUsage
ThisresultedinUserGeneratedContent(UGC)
CompaniesorIndividualscantakeadvantageofUGC
toexplorePublicOpinionandSentiment.
ActiveUsers(Monthly)

5. Methodologyused:SentimentAnalysisandTopicModelling.
Toexplorethetopicfromamassiveamountofdata(BigData),itrequirespropermethodology
thathavethecapabilitytoquicklyextractmassiveinformationcontained,withlesseffortand
cheaperprice.*
*ComparedtoConventionalMethodology

6. ProblemStatement
SocialMediaUsage UserGeneratedContent
Whichtakeslongertimetomapthetopicsandsentiment.
Currentlyusesconventionalway(Questionnaire,Interview,Media,etc.)
Requiresmethodsthatabletoextract
andanalyzecontent.

7. ResearchFramework

8. ResearchStages

9. Sample
Population UserofTwitter
UsersdiscussingUber

10. 1.048.576Whoa! That’s a big number, aren’t you proud?
10
tweets
10thFebruary2017until28thFebruary2017

11. DataAnalysisTechniques
SentimentAnalysis TopicModelling
1.TrainingDataGeneration
2.Preprocessing
(Tokenization,Stopwords,Stemming)
3.Dailybasisdatasetseparation
4.Weighting(TF-IDF)
5.MachineLearning(NaïveBayes)
6.PerformanceEvaluation&Performance(ConfusionMatrix,Accuracy,
F-Measure&Kappa)
7.SeparatePositive,NegativeandNeutralintodifferentfiles.
8.TopicModelling(LDAvis)

12. Results SentimentAnalysis
Based on the study of Ting,Tsang, &H.C (2011), the data is used forthe establishment of
training data as much as 30% and70% as test data.

13. Text Category
I am a ram guy but I totally appreciate vintage sports cars and that 1950s
corvette is totally amazing!
POSITIVE
when will gm step up and do the right thing and recall the defective dash
in the 07-14 trucks? I have bought many gm trucks and am honestly
ashamed that you cannot stand behind a product better than this.
NEGATIVE
Textareclassifiedintothreecategory:Positive(+)andNegative(-)
Classification

14. DynamicSentimentRatingResults

15. February24thPositivesentimentanalysisresult
February24thNegativesentimentanalysisresult
Ubercurrentlyfocusonincreasingthenumberofusersaroundtheworld.Uberformulates
astrategytoattractpeopleon usingUbertocommute,one ofthestrategiesistogivea
discounttotheconsumerintheformofpromo-code.Interestingly,thestrategyisrelatively
successful,itisprovedby thetopic modelingoutputshowsthaton24thFebruary2017,
positivelytalkedtopicsarearoundfree-codeorpromocode andtaxirides.
Ontheotherhand,thereisanincreasingnumberofnegativesentiment,themostdominant
negativetopicson 24thFebruary2017andinternalissuesthatappearon thenews,Uber
firesexecutiveaccusedofstealingGoogle’sself-drivingcarsecrets.Fora thorough
overviewofthedataset,weconducttopic modelingforthewhole datasettocapturethe
whole topicinthedataset.

16. Results TopicModelling
To map the detailed topic, the author separate sentiment analysis result into2 files (positive andnegative). Neutral classified text is
not used since it contains a lotof data noises (e.g. advertisement, no sentiment words, etc.), except for Instagram, the authorwill
focus on neutral, because it has relatively faramount between positive, negative andneutral, so we can analyse the neutral data to
findout the wordorfrequently used hashtags. Then list the most probableterms within topics to summarize the topic.

17. 1) The first dominant positive topics in the dataset are about the free rides, promo. We
interpret this topic as an information in which most of the users tweeted about the promotion of
Uber,thepublicisreactingpositivelytowardthepromotion.
2) Second dominant positive topic in the dataset is about the driver compliment, the
users express their feelings on the usage of the services. Most of the customer is satisfied with
theservice,thiscanbe interpretedfromthewordlove,good, drivers.
Twitter-Uber:Positive

18. 1) There is a negative sentiment that influences the customer opinion. Based on the visualization
above, the first dominant negative topic is about sexual-harassment is probably occurred by the
news that is stating that there is a sexual-harassment towards Uber’s employee. These issues are
revealed by former Uber engineer. then it is followed by the claim of the Uber CEO who conduct the
investigation.
2) The second negative sentiment on the dataset is related to the driver. Even though some people
satisfied with the service, there are several complaints on its service, some ofthe drivers have been
reported driving while he/she is drunk. The customer is unsatisfied with the fact that the driver
tends to talk with the customer they expect to remain silent or calm, this is referred to driver talk
shitstop.
Twitter-Uber:Negative

19. CONCLUSION
We havesuccessfullyimplementedthemethodtoproperlyanalyze,summarizeand extractmassivescaletweets.Inourlarge-scalecasestudyregardingUber.NaïveBayesmethod
issuitableforsentimentanalysisaswellastheLDAmethodthathasprovencapabilityinextractingin-depthinsightaboutthetopicsdiscussedinthelarge-scaledataset.Inour
opinion,themethodthatweuseinthisresearcharebetterinthetermofreal-timeprocessingcapabilitycomparedtothetraditionalway.
Theresultcompletewithinlessthan5-10minutes(onlyprocessingtime– dataalreadypreprocessed).Comparedtoconventionalmethodofinsightextraction,dynamicsentiment
analysis(sentimentbasedon adailybasis)iscapableintheextractionofopinionin massivedata,thiscreatesmoreeffectiveandefficientprocess.Astheglobalizationeraevolves,
speedisone mostessentialfactorin business,abusinessorganizationhastoquicklyreacttotheircustomer’sopinion.
Asfortheshortcomingsofthisresearch,itmay needmoreresearch.Therefore,wecan finda methodtocalculate theaccuracyoftheTopic Modelingand themethodtocleandata
effectively.Oursuggestionforfutureresearch,thestopworddictionarymustbeenriched,reduce theambiguityofthetext in ordertoincreasetheaccuracy,usingt-SNEtomodel
topicwithevenmoredetailedresults,whichcanhavemorecustomizableparameter(e.g.MultidimensionalScalingMethod,TopicalDistanceCalculation,NumberofClusters,
NumberofTerms).Furthermore,furtherresearcherneedtohavein-depthdiscussionon howthismethodcan beimplementedinbusinessanalytics.

20. The world’s most valuable resource
is no longer oil, but data.
www.economist.com
“
BIG DATA & SOCIAL COMPUTING LABORATORY 2017
SCHOOL OF ECONOMICS AND BUSINESS
TELKOM UNIVERSITY