Opinion Mining on Hotel Reviews

CSE-4250: PROJECT AND THESIS-I

Presented ByMd. Rafeedul Bar Chowdhury ID:11.02.04.088

Zahidul Haque ID:11.02.04.086Mahmud Hossain ID:11.02.04.082

Soumik Das Bibon ID:11.02.04.003

2

Course Teacher

MIR TAFSEER NAYEEM

Lecturer, Dept. of Computer Science & EngineeringAhsanullah University Of Science & Technology

18-Jun-15

3

What is Opinion?Opinions are thoughts influencing decision making.

• Which schools should I apply to?• Which professor to work for?• Whom should I vote for?• Which hotel should I book?

18-Jun-15

4

Whom shall I ask for opinions?Pre Web

Friends and relatives Acquaintances

Post Web Blogs (google blogs, livejournal) E-commerce sites (amazon, ebay) Review sites (CNET, PC Magazine) Discussion forums (forums.craigslist.org,

forums.macrumors.com)18-Jun-15

5

Have enough opinions!

Now that I have got enough opinions, I can take decisions…

Is it really enough?

18-Jun-15

6

…Having opinions is not enough Searching for reviews is quite difficult

Searching for opinions is not as convenient as general web search

Huge amounts of information available on one topic Difficult and quite impossible to analyze each and every review separately Expression of reviews are different in many ways “overall, this hotel is my first choice at Cox’s Bazar…”

“the facilities are good but the service isn’t so…”

“best in Cox’s Bazar by quality and value”

“…disappointing”18-Jun-15

7

What is Opinion Mining?Computational study of opinions, sentiments and emotions expressed in text.

Detects the contextual polarity of text (positive or, neutral or, negative)

Derives the opinion, or the attitude of an opinion holder.

18-Jun-15

8

Mining opinions…

Its about finding out what people think…

18-Jun-15

9

Topic of our researchMining traveler experiences/reviews expressed on services provided by hotels in Bangladesh

18-Jun-15

10

Our research domain We are mining reviews from the travelling guide, “TripAdvisor” www.tripadvisor.com/Bangladesh

We are starting our work from the district that attracts tourists the most which is Cox’s Bazar

Primarily we are mining reviews of travelers visiting various hotels in this tourist gathering area

18-Jun-15

11

TripAdvisor

2

3

1

18-Jun-15

12

TripAdvisor(Cont.)

1) Searching panel for hotels, situated on different locations/arease.g. Search Cox’s Bazar, Bangladesh, Asia for Long Beach Hotel

2) Panel for booking hotels based on prices

3) Searching Panel for a sorted list of hotels based on particular preferencese.g. types of hotel, most rated hotels etc.

18-Jun-15

13

TripAdvisor(Cont.)

1

2

18-Jun-15

14

TripAdvisor(Cont.)

1) List of hotels sorted by traveler ratings

2) Panel where travelers checks vacancy and books hotels e.g. Check-In Date/Check-Out Date of travelers

18-Jun-15

15

TripAdvisor(Cont.)

1 2

318-Jun-15

16

TripAdvisor(Cont.)

1) Hotel rating in stars(out of 5) based on type of reviews (positive or, negative) given by travelers e.g. Rating: 4.5 out of 5 stars; 95 Reviews

2) Position of the hotel among other hotels in an area depending on positive feedbacks from the travelerse.g. No.1 of 24 hotels in Cox’s Bazar

3) Special services provided by the hotele.g. Free parking, Free breakfast etc.

18-Jun-15

17

Reviewers of TripAdvisor

1 3

52

4

18-Jun-15

18

Reviewers of TripAdvisor(Cont.)

1) Name and residence of the reviewing traveler e.g. MUHassan, Dhaka City, Bangladesh

2) Information about the profile of the reviewer on “TripAdvisor” site e.g. Top Contributor, 50 Reviews, 19 helpful votes etc.

3) Rating provided by the reviewer and the time of the reviewe.g. 5 out of 5 stars, Reviwed 12,June,2015 etc.

4) The review of the traveler in a large opinioned paragraph

5) Voting panel for other users to vote a review if it is helpful or not!18-Jun-15

19

Reviewers of TripAdvisor(Cont.)

1

2

18-Jun-15

20

Reviewers of TripAdvisor(Cont.)

1) All the reviews provided by a certain reviewer on the site

2) Feedback given by a hotel manager upon reviewing of a traveler

18-Jun-15

21

Problem domain of our research

Integrity of the reviews can not be ensured

Due to competition among the hotels high possibility of false reviews

Amount of reviews highly varies which sometimes results in inaccurate ratings of hotels

18-Jun-15

22

Solution to these problems… Only the reviews given by verified travelers

will be prioritized first e.g. Top Contributor, Contributor etc.

For the rating system to work there has to be at least 10 or more reviews

18-Jun-15

23

Goal of our research

Rating the hotels according to reviews based on potential opinion holders

Finding out the hotels which impacts the most upon the traveling tax

Mining each individual reviews then summarizing them according to polarity to get accurate opinion about a hotel

18-Jun-15

24

Works we have done so far… Primarily we have parsed data from popular hotels in Cox’s Bazar

We have mined and stored data of 15 hotels initially

This data will work as our data set to further our research

which contains reviews and reviewers information

We have collected another data set from National Board of Revenue(NBR) about the revenue on traveling tax

18-Jun-15

25

Tools for Parsing data

The language we used for parsing data,-python(2.7.9)

https://www.python.org/

We used a library for extracting data from HTML and XML files,-beautifulsoup (vers. 4)

18-Jun-15

26

Flowchart for parsing data

18-Jun-15

27

Code for parsing data

18-Jun-15

28

Code for parsing data(Cont.)1) ‘head’ : this variable stores headline of the reviews2) ‘date’ : time when the review was given3) ‘member_info’ : name and residence of the reviewer4) ‘member_badge’ : Information about reviewers profile

e.g. Top Contributor, 50 Reviews etc.5) ‘mngr_com’ : Feedbacks on reviews from hotel managers6) ‘review’ : The whole review will be stored in this variable

12

34

56 18-Jun-15

29

Data set created from the parsed data

1

2

3

5

4

18-Jun-15

30

Data set on traveling tax

This is our data set which will be used to calculate the impact of hotels on traveling tax

It contains data on collected revenue from the tourism sectortill fiscal year ’13 - ’14 to ’14 - ’15

18-Jun-15

31

Data set on traveling tax(Cont.)

18-Jun-15

Why Prioritize Opinions?

Opinion prioritizing is a must in order to distinguish mature and professional reviewers opinion from an amateur reviewers opinion.

A professionals point of view and interest will definitely make a difference in case of ranking a hotel.

There will always be some integrity/accuracy issues when it comes to opinions as someone stating a feature as satisfactory while other will state otherwise ( Whose opinion to choose? )

Prioritizing Opinions(Cont.) In TripAdvisor, there is already a priority rating system available for

the reviewers!

1. Reviewer: (3 – 5) reviews 2. Senior Reviewer: (6 – 10) reviews 3. Contributor: (11 – 20) reviews 4. Senior Contributor: (21 – 49) reviews 5. Top Contributor: (50+) reviews

Prioritizing Opinions(Cont.) For the prioritizing purpose, we gave a potential opinion holders value

for each class of reviewers.

1. Reviewer: 22. Senior Reviewer: 4 3. Contributor: 5 4. Senior Contributor: 7 5. Top Contributor: 10

This value indicates which reviewer gets more priority than others.

Opinion Subjectivity

Opinion Subjectivity is categorizing reviews as positive, negative or objective. Methods we are using for calculating opinion subjectivity:

-SentiWordNet -Semi Supervised Learning Approach -Naive Bayes Method

SentiWordNet

It is a lexical resource that is open publically for opinion mining

It scores every word in the database as positive(+) negative(-) or objective/neutral values.

Semi Supervised Learning Approach

In the beginning synsets is labeled manually.i.e SentiWordNet in our case

Labeling will be expanded by machine learning approach. i.e we are using Naive Bayes Method

This process is efficient and helps avoiding labeling errors.

Naive Bayes Method This method is used to predict the values of words that are not in the

database or synsets. The main formula is:

Where,P(c) = Probability of Unlabeled data(c)P(d) = Probability of Labeled data(d)

Final Calculation

The formula we will use for ranking hotel is:

Z =

Where,Z is the deciding value for an individual hotel, which will place it in the appropriate position in the ranks.

For instance: Let’s assume:

Hotel 1: Long Beach Hotel Hotel 2: Resort Labiba BilashPotential Opinion Holders value: 7 Potential Opinion Holders Value: 2Opinion Subjectivity: +15.7 Opinion Subjectivity: +12.3

For, Hotel 1: Z = 7 * (+15.7) For, Hotel 2: Z = 2 * (+12.3)= 109.9 = 24.6

So, Hotel 1 will be ranked above Hotel 2. (assuming there is only one review in each hotels)

41

References [1] B. Liu, “Sentiment Analysis and Subjectivity.” A Chapter in

Handbook of Natural Language Processing, 2nd Edition, 2010. http://www.cs.uic.edu/~liub/

[2] Kavita Ganesan , Hyun Duk kim, “Opinion Mining-A Short Tutorial”.

[3] Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan, “Thumbs up? sentiment classification using machine learning techniques” 2002.

[4] http://sentiwordnet.isti.cnr.it/

[5] https://en.wikipedia.org/

18-Jun-15

4218-Jun-15