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datasetMicrosoft Word - BUS5CA Assignment1 2019 [20190822].docx 	 1	 BUS5CA	Customer	Analytics	and	Social	Media	 Semester	2	2019	 	 Assignment	1	 Social	Media	Analysis	for	Understanding	Customer	Preferences	and	Sentiments	 	 	 Release	Date:	22nd	August	2019	 Due	Date:	9th	September	2019	@	9:00am	 Assignment	Type:	Individual	 Weight:	30%	 Format	of	Submission:	A	report	(electronic	form)	and	 electronic	submissions	of	project	files	 (SAS	project	files	and	R	scripts)	in	LMS	site.	 	 	 Learning	Objective:	 The	learning	objective	of	Assignment	1	is	to	further	develop	your	understanding	and	skills	on	social	 media	analytics	via	performing	analysis	on	two	case	studies:	 	 	 1. Case	Study	A:	you	will	work	as	a	social	marketing	analyst	in	a	consulting	company	to	 uncover	 the	 impacts	of	online	advertising	and	communication	with	customers.	 The	 aim	 of	 the	 study	 is	 to	 educate	 the	 marketing	 teams	 of	 their	 clients	 (in	 diverse	 industries)	 to	 market	 their	 products	 and/or	 services	 on	 social	 media	 to	 maximise	 customers’	involvement	(positive	interest	and	sharing).	The	company	is	interested	in	 finding	out	the	relationship	between	the	keywords,	shares,	sentiments	and	whether	 there	 is	 a	 relationship	 in	 different	 topic	 categories	 such	 as	 entertainment,	 technology,	 business,	 etc.	 that	 are	 of	 interest	 to	 different	 clients	 in	 various	 industries.	 	 2. Case	Study	B:	you	will	be	a	data	scientist	working	for	a	hotel	review	firm	to	develop	a	 sentiment	analytics	engine	 for	Twitter,	which	 is	used	 to	predict	 consumers’	 review	 sentiments.	The	aim	is	to	develop	both	dictionary-based	and	machine	learning-based	 sentiment	analytics	scripts	using	a	number	of	R	libraries	and	SAS	Sentiment	Analysis	 Studio	(covered	in	the	workshop	activities	on	Week	4	and	Week	5).	You	are	required	 to	use	the	developed	engine	to	predict	hotel	reviewers’	sentiments	and	benchmark	 various	algorithms	and	analytics	tools.		 	 	 	 	 	 2	 Case	Study	A	(15%)	 	 Leveraging	the	power	of	content	and	social	media	marketing	can	help	elevate	the	audience	 and	customer	base	 in	a	dramatic	way.	However,	using	social	media	 for	marketing	without	 any	previous	experience	or	 insight	could	be	challenging.	 It	 is	vital	 for	a	marketing	team	to	 understand	social	media	marketing	fundamentals.	If	a	company	publish	exciting,	high-quality	 content	and	build	an	online	audience	of	quality	followers,	they	can	share	it	with	their	own	 follower	audience	on	Twitter,	Facebook,	LinkedIn,	Google+,	their	own	blogs	and	many	other	 social	media	platforms.	This	sharing	and	discussing	of	content	open	up	new	entry	points	for	 search	engines	like	Google	to	find	it	in	a	keyword	search.	Those	entry	points	could	grow	to	 hundreds	or	 thousands	or	more	potential	ways	 for	 people	 to	 find	 a	 company,	 product	or	 service	 online.	 Finding	 and	 understanding	 the	 online	 influencers	 in	 the	market	who	 have	 quality	audiences	and	are	 likely	 to	be	 interested	 in	 the	product,	 service	or	business	 could	 make	a	huge	positive	impact.		 	 The	 consulting	 company	 collected	 information	 on	 articles	 that	were	 shared	 by	 people	 on	 social	media.	The	dataset	contains	approximately	39000	articles	and	a	 large	number	(with	 the	total	of	31)	of	features	were	extracted	from	the	HTML	code	of	the	article,	including	the	 title	 and	 the	 content	 of	 each	 article.	 (The	 description	 of	 the	 dataset	 is	 provided	 as	 an	 appendix.)	Some	of	the	features	depend	on	characteristics	of	the	service	used,	which	could	 be	 analysed	 based	 on	 the	 meta-data	 provided:	 articles	 have	 the	 meta-data,	 such	 as	 keywords,	 data	 channel	 type	 and	 the	 total	 number	 of	 shares	 (on	 Facebook,	 Twitter,	 Google+,	 LinkedIn,	 Pinterest),	 etc.	 The	 data	 channel	 categories	 are:	 ‘Lifestyle’,	 ‘Business’,	 ‘Entertainment’,	 ‘Social	 Media’,	 ‘Technology’,	 and	 ‘World’.	 In	 addition,	 several	 natural	 language	processing	features	were	also	extracted.	 	 	 Task	Requirements	 	 As	a	data	analytics	team	member	for	the	consultancy	firm,	you	are	required	to	carry	out	a	 number	of	data	analytics	tasks	for	the	consulting	company	using	the	data	collected.	You	are	 given	access	to	a	sample	of	the	data	where	some	of	the	variables	have	been	removed	as	they	 are	not	considered	important	for	the	analysis	of	this	assignment.	 	 The	company	is	interested	in	identifying	for	each	data	channel:	 • Investigate	the	impact	of	the	article	properties	on	sharing;	 • Use	the	SAS	Text	Miner	for	text	analysis	to	identify	key	features	in	the	articles	and	 analyse	their	contribution	towards	low	and	high	sharing.	 	 To	achieve	the	above,	you	need	to	carry	out	the	following	data	analytics	tasks:	 	 a) Task	1:	Explore	the	impact	of	article	properties	(7%)	 	 Explore	the	data	and	 investigate	what	properties	of	the	 article	correlate	with	the	high	 number	of	shares	of	the	article	on	social	media.	 • Open	the	dataset	‘online_news_popularity.xlsx’	using	Microsoft	Excel.	 • Explore	 the	 dataset	 to	 understand	 and	 manage	 the	 six	 types	 of	 data	 channels	 (lifestyle,	entertainment,	bus,	socmed,	tech,	world)	and	the	associating	data.	In	each	 	 3	 data	 channel	 column,	 the	 value	 of	 1	 represents	 that	 the	 data	 in	 the	 row	 is	 of	 the	 corresponding	data	channel.	 • Copy	the	separate	datasets	for	each	channel	to	different	Excel	sheets	(sort	and	filter	 by	each	data	channel	to	separate).	 • In	 each	 data	 channel,	 identify	 the	 articles	 with	 a	 high	 number	 of	shares	 (with	 the	 threshold	of	top	10%	in	the	dataset).	 • Investigate	 the	 following	 properties	 and	 explain	 how	 they	 could	 have	 affected	 the	 high	number	of	shares.	You	should	provide	the	explanation	to	support	your	argument.	 o Number	of	tokens	in	the	title	 o Number	of	tokens	in	the	content	 o Was	the	article	published	on	the	weekend	 o Number	of	links	 o Number	of	images	 o Number	of	videos	 (Hint:	To	do	this,	you	can	create	plots	in	R	between	the	corresponding	columns	and	the	 number	of	shares.	You	may	want	to	include	a	fitted	line	to	your	plots	to	investigate	the	 correlation	for	continuous	variables.)	 	 2. Task	2:	Use	SAS	Text	Miner	for	keyword	analysis	(8%)	 • Use	the	SAS	Text	Miner	to	extract	the	keywords	from	the	title	 in	each	data	channel.	 (Hint:	To	do	this,	you	can	refer	to	the	workshop	activities	in	Week	3	and	Week	4;	by	 setting	‘Title’	column	as	the	only	‘Text’	role	in	the	variable	setting.)	 • What	are	the	highly	used	(top	10)	topics	in	each	category?	Use	the	SAS	Result	window	 to	explain	your	answers.	 (Hint:	‘Topic’	column	will	need	to	be	set	as	the	only	‘Text’	role.)	 • Are	 there	 common	 topics	 which	 span	 across	 data	 channels	 and	 relate	 to	 a	 high	 number	of	shares	and	a	low	number	of	shares?	Use	the	whole	dataset	in	the	SAS	Text	 Miner	to	identify	the	relationship.	You	should	provide	the	explanation	to	support	your	 argument.	 (Hint:	Use	the	whole	dataset	to	 identify	the	articles	with	the	high	number	of	shares	 and	 the	 low	number	of	 shares	 –	by	using	 appropriate	 thresholds	with	 the	 top	10%	 and	 the	bottom	10%	 in	 the	dataset.	 Separate	 the	dataset	using	Excel	based	on	 this	 before	the	analysis	and	use	these	two	datasets	to	analyse	the	common	topics	in	each	 of	 them.	 In	 this	 question,	 please	use	 ‘Title’	 column	as	 the	only	 ‘Text’	 role	 for	 topic	 modelling.)	 	 	 You	are	required	to:	 	 a) Prepare	a	report	for	the	Case	Study	A	with	all	the	analytics	results	to	the	above	two	 key	 tasks.	 (You	 can	 use	 an	 appendix	 for	 any	 additional	 screenshots,	 figures	 and	 tables,	which	you	feel	are	important	for	the	report).	The	report	should	be	named	as:	 								Assignment1A_Report.doc	 b) Save	the	R	script	after	Task	1	above	as:		Assignment1A.r	 c) Save	the	SAS	project	 for	Task	2	above	as	Assignment1_Task1.spk.	You	 may	 zip	 the	SPKs	 files	 if	you	have	multiple	of	them.	The	SAS	project	 file	 should	be	 named	as:	 Assignment1_SAS1.zip	 	 	 4	 Case	Study	B	(15%)	 	 Sentiment	analysis	is	the	technique	aiming	to	gauge	the	attitudes	of	customers	in	relation	to	 topics,	products	and	services	of	interests.	It	is	a	pivotal	technology	for	providing	insights	to	 enhance	the	business	bottom	line	in	campaign	tracking,	customer-centric	marketing	strategy	 and	 brand	 awareness.	 Sentiment	 analytics	 approaches	 are	 used	 to	 produce	 sentiment	 categories	such	as	‘positive’,	‘negative’	and	‘neutral’.	More	specific	human	emotions	are	also	 the	topic	of	interest.	 There	are	two	major	streams	of	methods	to	develop	sentiment	analytics	 engine:	the	dictionary-based	and	machine	learning-based	approaches.	In	this	assignment,	you	 are	required	to	perform	sentiment	analytics	based	on	both	approaches.	 	 	 Task	Requirements	 	 As	a	data	scientist,	you	are	required	to	perform	a	number	of	data	analytics	tasks.	You	are	 tasked	to	develop	both	dictionary-based	and	machine-learning	sentiment	analytics	engines	 using	R	programming	language	and	apply	it	to	predict	the	sentiments	of	hotel	review	tweets	 from	a	sample	of	data.	You	are	also	 required	 to	use	 the	SAS	Sentiment	Analysis	Studio	 to	 compare	the	results.	 	 To	achieve	the	above,	you	need	to	carry	out	the	following	data	analytics	tasks:	 	 1. Develop	 a	 dictionary-based	 sentiment	 analytics	 engine	 based	 on	 the	 R	 library	 ‘syuzhet’	to	analyse	the	different	emotions	from	hotel	review	tweets	(5%).	 • Analyse	and	aggregate	the	eight	emotions	(anger,	anticipation,	disgust,	fear,	joy,	 sadness,	surprise	and	trust)	from	the	hotel	review	tweets	file	‘hotel_tweets.csv’	 using	the	function	‘get_nrc_sentiment’.		 • You	are	 required	 to	plot	a	chart	 to	visualise	 these	emotions	using	 the	R	 library	 ‘ggplot2’.		 • You	 should	 combine	 both	 negative	 and	 positive	 tweets	 into	 one	 before	 conducting	the	analysis.	 	 2. Develop	a	machine	learning-based	model	using	the	R	libraries	‘tm’	and	‘e1071’	as	 well	as	evaluate	the	predictive	accuracies	of	SVM	classifier	(5%).	 • Develop	 R	 scripts	 and	 import	 the	 data	 set	 ‘hotel_tweets.csv’	 for	 training	 and	 testing.		 • Use	the	first	200	negative	tweets	and	the	first	200	positive	tweets	as	the	training	 dataset;	and	use	the	rest	of	the	63	negative	tweets	and	63	positive	tweets	as	the	 testing	dataset.	 (Hint:	You	may	need	to	use	as.character()	function	to	convert	a	dataframe	column	 from	factors	to	characters.)	 • Develop	 a	 machine	 learning-based	 sentiment	 analytics	 engine	 and	 predict	 sentiment	 categories	 (only	 ‘positive’	 and	 ‘negative’)	 using	 ‘tm’	 and	 ‘e1071’	with	 the	SVM	classifier.	 • Evaluate	the	testing	accuracies	and	report	the	predicted	results.	 	 	 	 5	 3. Develop	a	statistical	model	using	SAS	Sentiment	Analysis	studio	and	evaluate	the	 accuracies	(5%).	 • Use	the	data	folder:	‘hotel_tweets’	which	contain	‘negative’	and	‘positive’	tweets	 for	training	and	testing.	 • Build	 a	 statistical	 model	 using	 SAS	 Sentiment	 Analysis	 (either	 simple	 or	 advanced),	you	may	change	configurations	in	the	advanced	model	to	obtain	the	 best	training	accuracy.	 (Hint:	Refer	to	the	SAS	Sentiment	Analysis	Studio	tutorial.)	 • Evaluate	and	compare	the	testing	accuracies	for	different	models	and	report	the	 results.	 • Compare	this	result	with	the	previous	predictive	results	using	R	and	discuss.	 	 You	are	required	to:	 	 a) Prepare	a	report	for	Case	Study	B	with	all	the	analytics	results	to	the	above	three	key	 tasks.	 (You	 can	 use	 an	 appendix	 for	any	additional	 screenshots	which	you	feel	 are	 important	for	the	report).	The	report	should	be	named	as:	 Assignment1B_Report.doc	 b) Save	the	R	script	after	Task	2	above	as:		Assignment1B.r	 c) Save	the	SAS	Sentiment	Studio	project	as:	Assignment1_SAS2.zip		 	 Important:	You	should	submit	all	the	reports,	R	scripts	and	SAS	Sentiment	Studio	project	 via	the	LMS	Assignment1	submission	link.	 	 Report	Guidelines	 	 1. The	 report	 should	 consist	 of	 a	 table	 of	 contents,	 an	 introduction,	 and	 logically	 organised	sections/topics	(such	as	‘case	study	A’,	‘case	study	B’),	a	conclusion	and	a	list	 of	references	where	necessary.	 2. Choose	a	fitting	sequence	of	sections/topics	 for	the	body	of	the	report.	Two	sections	 for	 the	 two	 case	 studies	 are	 essential,	 you	 may	 add	 other	 sub-sections	 deemed	 relevant.	 3. You	must	include	diagrams,	tables	and	charts	from	the	analytics	solutions	to	effectively	 present	 your	 results.	 (Consider	 using	 Alt	 +	 Print	 Screen	 to	 capture	 screenshots	 if	 needed.)	 4. Page	limit:	For	each	case	study,	five	(5)	pages	for	report	writing	but	not	more	than	ten	 (10)	pages	including	appendices.	 5. Reports	should	be	written	 in	Microsoft	Word	(font	size	11)	and	submitted	as	a	Word	 file.	 6. Final	submission	will	comprise	six	separate	files:	 a. Assignment1A_Report.doc	(should	not	be	zipped);	 b. Assignment1B_Report.doc	(should	not	be	zipped);	 c. Assignment1A.r;	 d. Assignment1B.r;	 e. Assignment1_SAS1.zip;	 f. Assignment1_SAS2.zip.	 	 6	 Marking	Rubrics	 	 A	grade	will	be	awarded	to	each	of	the	tasks	and	then	an	overall	mark	determined	for	the	 entire	assessment.	The	rubric	below	gives	you	an	idea	of	what	you	must	achieve	to	earn	a	 certain	‘grade’.	 	 As	a	general	rule,	to	meet	a	‘C’,	you	must	first	satisfy	the	requirements	of	a	‘D’.	And	for	an	 ‘A’,	 you	must	 first	 satisfy	 the	 requirements	of	 a	 ‘B’,	which	must	of	 course	 first	meet	 the	 requirements	of	a	‘C’	and	so	on.	 	 The	marking	rubric	for	this	assignment	is	given	below.	 	 Criterion	 Pass	 Credit	 Distinction	 High	Distinction	 Case	study	one:	 Limited	effort	to	 Fair	effort	to	 Excellent	effort	to	 Exceptional	effort	to	 Impact	of	article	 structure	and	present	 structure	and	present	 structure	and	present	 structure	and	present	 properties	 information	and	 information	and	 information	and	 information	and	 (7	marks)	 insights.	 insights.	 insights.	 insights.	 Case	study	one:	Use	 Limited	effort	to	 Fair	effort	to	 Excellent	effort	to	 Exceptional	effort	to	 SAS	Text	Miner	for	 structure	and	present	 structure	and	present	 structure	and	present	 structure	and	present	 keyword	analysis	 information	and	 information	and	 information	and	 information	and	 (8	marks)	 insights.	 insights.	 insights.	 insights.	 	 Limited	knowledge	of	 Fair	knowledge	of	 Excellent	knowledge	 Comprehensive	 	 SAS	Text	Miner.	 SAS	Text	Miner.	 of	SAS	Text	Miner.	 knowledge	of	SAS	 	 	 	 	 Text	Miner.	 Case	study	two:	 Develop	dictionary- based	sentiment	 analytic	engine	and	 analyse	emotions		 (5	marks)	 Limited	effort	to	 Fair	effort	to	 Excellent	effort	to	 Exceptional	effort	to	 structure	and	present	 structure	and	present	 structure	and	present	 structure	and	present	 insights	for	emotions	 insights	for	emotions	 insights	for	emotions	 insights	for	emotions	 from	tweets.	 from	tweets.	 from	tweets.	 from	tweets.	 Limited	knowledge	of	 Fair	knowledge	of	the	 Excellent	knowledge	 Comprehensive	 the	R	programming.	 R	programming.	 of	the	R	 knowledge	of	the	R	 	 	 programming.	 programming.	 Case	study	two:	 Develop	machine	 learning-based	 sentiment	 analytic	engine	and	 evaluate	predictive	 accuracies	using	R	 (5	marks)	 	 Limited	effort	to	 Fair	effort	to	 Excellent	effort	to	 Exceptional	effort	to	 structure	and	present	 structure	and	present	 structure	and	present	 structure	and	present	 information	and	 information	and	 information	and	 information	and	 insights.	 insights.	 insights.	 insights.	 Limited	knowledge	of	 Fair	knowledge	of	the	 Excellent	knowledge	 Comprehensive	 the	R	programming.	 R	programming.	 of	the	R	 knowledge	of	the	R	 	 	 programming.	 programming.	 Case	study	two:	 Limited	effort	to	 Fair	effort	to	 Excellent	effort	to	 Exceptional	effort	to	 Develop	sentiment	 structure	and	present	 structure	and	present	 structure	and	present	 structure	and	present	 analytic	engine	 information	and	 information	and	 information	and	 information	and	 using	SAS	Sentiment	 insights.	 insights.	 insights.	 insights.	 Analysis	Studio	 Limited	knowledge	of	 Fair	knowledge	of	 Excellent	knowledge	 Comprehensive	 (5	marks)	 SAS	Sentiment	 SAS	Sentiment	 of	SAS	Sentiment	 knowledge	of	SAS	 	 Studio.	 Studio.	 Studio.	 Sentiment	Studio.	 	 	 Other	information	 	 • Standard	 plagiarism	 and	 collusion	 policy,	 and	 extension	 and	 special	 consideration	 policy	of	this	university	apply	to	this	assignment.	 • A	cover	sheet	is	NOT	required.	By	submitting	your	work	online,	the	declaration	on	the	 university’s	assignment	cover	sheet	is	implied	and	agreed	to	by		you.	 	 7	 Appendix	–	Attribute	Information	 	 1. This	 section	 contains	 a	 description	 of	 the	 attributes	 of	 the	 dataset	 ‘online_news_popularity.xlsx’.	 {‘name	of	the	column’:	‘description’}	 	 1. url:	URL	of	the	article	(unique)	 2. title:	Title	of	the	article	 3. topic:	topics	related	to	the	article	 4. content:	content	of	the	article	 5. timedelta:	Days	between	the	article	publication	and	the	dataset	acquisition	 6. n_tokens_title:	Number	of	words	in	the	title	 7. n_tokens_content:	Number	of	words	in	the	content	 8. n_unique_tokens:	Rate	of	unique	words	in	the	content	 9. n_non_stop_words:	Rate	of	non-stop	words	in	the	content	 10. n_non_stop_unique_tokens:	Rate	of	unique	non-stop	words	in	the	content	 11. num_hrefs:	Number	of	links	 12. num_self_hrefs:	Number	of	links	to	other	articles	published	by	Mashable	 13. num_imgs:	Number	of	images	 14. num_videos:	Number	of	videos	 15. average_token_length:	Average	length	of	the	words	in	the	content	 16. num_keywords:	Number	of	keywords	in	the	metadata	 17. data_channel_is_lifestyle:	Is	data	channel	'Lifestyle'?	 18. data_channel_is_entertainment:	Is	data	channel	'Entertainment'?	 19. data_channel_is_bus:	Is	data	channel	'Business'?	 20. data_channel_is_socmed:	Is	data	channel	'Social	Media'?	 21. data_channel_is_tech:	Is	data	channel	'Tech'?	 22. data_channel_is_world:	Is	data	channel	'World'?	 23. weekday_is_monday:	Was	the	article	published	on	a	Monday?	 24. weekday_is_tuesday:	Was	the	article	published	on	a	Tuesday?	 25. weekday_is_wednesday:	Was	the	article	published	on	a	Wednesday?	 26. weekday_is_thursday:	Was	the	article	published	on	a	Thursday?	 27. weekday_is_friday:	Was	the	article	published	on	a	Friday?	 28. weekday_is_saturday:	Was	the	article	published	on	a	Saturday?	 29. weekday_is_sunday:	Was	the	article	published	on	a	Sunday?	 30. is_weekend:	Was	the	article	published	on	the	weekend?	 31. shares:	Number	of	shares	 	 2. The	description	for	the	dataset	‘hotel_tweets.csv’.	 {‘name	of	the	column’:	‘description’}	 1. Negative:	negative	tweets	content	 2. Positive:	positive	tweets	content	 	 3. The	description	for	the	dataset	‘hotel_tweets’	folder.	 Same	content	as	the	dataset	in	(2),	grouped	as	negative	and	positive

Microsoft Word - BUS5CA Assignment1 2019 [ XXXXXXXXXX].docx 1 BUS5CACustomerAnalyticsandSocialMedia Semester22019 Assignment1...

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