Sentiment Analysis and Text Mining over Hotel's guests ferdbacks in TripAdvisor.

1. Sentiment Analytics
HOTEL INDUSTRY

2. Text Mining/SA for the Hotel Industry
• With the availability of huge volumes of text-based information freely
available on the Internet, text mining can be used by hoteliers to
develop competitive and strategic intelligence.
• Accurate and timely competitor and customer intelligence enhances
hotel effectiveness and customer satisfaction
• Similar to data mining, text mining explores data in text files to
establish valuable patterns and rules that indicate trends and
significant features about specific topics

3. Traditional BI vs New Analytics approach
0
10
20
30
40
50
60
70
80
90
100
Hotel Chain I Hotel Chain II Hotel Chain III Hotel Chain IV
Traditional analytics Sentiment Analysis Revenue change %

4. Concept of Sentiment Anlysis
• environmental scanning of
customer intelligence by
analyzing digital portals like
TripAdvisor
• acquiring customer intelligence
by analyzing social media
• 3improving efficiency of
internal knowledge
management by analyzing
internal data
Importance Volume
Trip Advisor 75 83
Travel Portals 15 10
Social Media 10 7

5. Sentiment Analytics process
• Data flow
architecture
• Data load from
defined sources
Extracting
data E
• Transform data
• Add business
logic
Transforming
dataT • Set Analytics
goal
• Define KPI and
Rapporting env.
Loading to
EDW L

6. Web scraping technique
PYTHON SCRIPT TO SCRAP DATA FROM TRIP ADVISOR

7. Making external data familiar
DS: TRIP ADVISOR DATA SENTIMENT ATTRIBUTES
Trip Advisor review content Trip Advisor sentiment

8. ETL framework
Extracting data
Transforming and cleaning
Structure for un-structured data
Loading in EDW
Build OLAP onTop
Automate the process

9. Front End Solution – Reviews on TA

10. Front End Solution – Sentiment Analytics

11. Front End Solution – Customer Surveys

12. Business Value of Sentiment Analytics –
Organisation perspective
The hospitality and restaurant industries also benefit greatly from using text analytics to listen to the
conversation. Much of the customer feedback for hotels, resorts, and restaurants takes place outside of
the customer-company conversation (ex:TripAdvisor). Reviews can be placed on a plethora of websites,
forcing companies to manually seek out and interpret the conversation. With automated text analytics tools,
a hotel can quickly and easily assess whether they should be spending money on new linens or pool
improvements.
Text analytics can be used to develop a better understanding of the likes, dislikes and motivations of the
customer. Changing loyalty program incentives to match customers’ desires can improve customer loyalty and
increase sales.
There are many other examples, and the uses of text analytics to listen to the conversation are essentially
limitless. And, there is significant value in listening to the conversation. The conversation is immediate –
people
are talking in the moment they have an experience, in the moment they interact with the brand or the
company.
They are having conversations to try and figure out which brands they trust and want to have as part of their
lives. While sales are a lagging indicator, discussions are a leading indicator.

13. Business Value of Sentiment Analytics –
Customer perspective
Humans are subjective creatures and opinions are important. Being able to interact with people on that level
has many advantages for information systems.

14. Besim Ismaili
Data Scientist,CIO of BeyondIT

15. Tom Olaf
Hammervold
CEO of BeyondIT