EN sentiment analysis: online review of Disneyland from tripadvisor.com

文章信息
作者： kaiwu
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1. Install python and IDE for python

1.1 install python

http://kaiwu.city/index.php/python3

Python Logo PNG

https://www.python.org/downloads/

1.2 IDE(Integrated Development Environment) for python

https://hackr.io/blog/best-python-ide 

An integrated development environment (IDE) is a software application that helps programmers to develop software efficiently. It's where you build your Python projects!

It increases developer productivity by combining common developer tools such as software editing, building, testing, debugging, and packaging in one easy-to-use graphical user interface (GUI).

Other popular features include code refactoring, code search, code auto-completion, and continuous integration/continuous deployment (CI/CD).

Regardless of your preferred programming language or type of software development, an IDE will be one of your go-to tools.  

Moving on to the IDE's cousin, the code editor.

Sometimes mistaken for IDEs, the main difference is that IDEs provide more powerful tools to simplify the coding process.

https://jupyter.org/

https://jupyter.org/install

JupyterLab

Install JupyterLab with pip:

pip install jupyterlab

Note: If you install JupyterLab with conda or mamba, we recommend using the conda-forge channel.

Once installed, launch JupyterLab with:

jupyter lab

 

Jupyter Notebook

Install the classic Jupyter Notebook with:

pip install notebook
 

To run the notebook:

jupyter notebook

2.An introduction to python

2.1 leap year

year = int(input("please type in a four digit year value "));
if year % 4 == 0 and year % 100 != 0:
    print(year, "is a leap year")
elif year % 100 == 0:
    print(year, "is not a leap year")
elif year % 400 ==0:
    print(year, " is a leap year ")
else:
    print(year, " is not a leap year ")

 

2.2 take control of excel

the hotel reservation dataset is downloaded from kaggle

https://www.kaggle.com/datasets/ahsan81/hotel-reservations-classification-dataset

we use a subset (199 records)of the hotel reservation dataset (36275 records).

cmd

install openpyxl library

pip install openpyxl

 

http://kaiwu.city/openfiles/Hotel_Reservations199.xlsx

import openpyxl as xl;

filename ="D:/tdata/hotel_reservation/Hotel_Reservations199.xlsx"
wb1 = xl.load_workbook(filename)
ws1 = wb1.worksheets[0]
mr = ws1.max_row
mc = ws1.max_column

print(mr)
print(mc)

from openpyxl import Workbook

# create a new work
wb2 = Workbook()
ws2 =  wb2.active

# i is the row index
for i in range (2, mr + 1):
    #rename the Excel new workbook based on the chapter titlte
    ws2.title = ws1.cell(row = i, column = 1).value
    
    # j is the column index 
    for j in range (1, mc):
        # copy the header of the table
        ws2.cell(row = 1, column = j).value = ws1.cell(row = 1, column = j).value
        # copy the i row
        ws2.cell(row = 2, column = j).value = ws1.cell(row = i, column = j).value

    # save the excel file
    wb2.save("D:/tdata/hotel_reservation/" + ws2.title + ".xlsx")
    # print the process
    print('the %d Excel file has been saved' % (i-1))

 

http://kaiwu.city/openfiles/python_loop_excel.ipynb

choose the weblink,

save link as..

 

3.sentiment analysis

3.1jupyter notebook

icon_ipynb.png

http://kaiwu.city/openfiles/EN_sentiment_analysis_Disneyland_tripadvisor.ipynb

 

3.2dataset

http://kaiwu.city/openfiles/DisneylandReviews.csv

 


https://github.com/DataScience-in-Tourism 

The sample dataset for this chapter includes a few rows. so in this file, the sample dataset has been replaced with the dataset from Disneyland on Kaggle (TripAdvisor). The codes has been adjusted accordingly, correcting a few mistakes.

 

download weblink

https://www.kaggle.com/datasets/arushchillar/disneyland-reviews

About Dataset
The dataset includes 42,000 reviews of 3 Disneyland branches - Paris, California and Hong Kong, posted by visitors on Trip Advisor.
Column Description:

Review_ID: unique id given to each review
Rating: ranging from 1 (unsatisfied) to 5 (satisfied)
Year_Month: when the reviewer visited the theme park
Reviewer_Location: country of origin of visitor
Review_Text: comments made by visitor
Disneyland_Branch: location of Disneyland Park

3.3datasets after data clean

Hong Kong Disneyland were with 786 reviews in 2019

http://kaiwu.city/openfiles/sentiment_hk_disneyland2019.csv

Hong Kong Disneyland were with 211 reviews in January 2019

http://kaiwu.city/openfiles/sentiment_hk_disneyland2019Jan.csv

 

3.4book chapter

   

http://kaiwu.city/openfiles/sentiment_analysis_Applied Data Science in Tourism - Roman Egger.pdf

 

参考资料：

Kirilenko, A. P., Wang, L., & Stepchenkova, S. O. (2022). Sentiment Analysis: Gaging Opinions of Large Groups. In R. Egger, Applied Data Science in Tourism: Interdisciplinary Approaches, Methodologies, and Applications (pp. 363–374). Springer International Publishing. https://doi.org/10.1007/978-3-030-88389-8_17

  

(1)springer

https://doi.org/10.1007/978-3-030-88389-8

(2)book site

http://www.datascience-in-tourism.com/

(3)github

https://github.com/DataScience-in-Tourism