Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets
Abstract
:1. Introduction
- (1)
- What are the prime concerns expressed in PORs during pre-COVID-19 and the first wave of the COVID-19 disease outbreak?
- (2)
- How do the hot topic keywords differ across the three stages?
- (3)
- How do the topics observe in PORs change by time and COVID-19?
- (4)
- How do PRWs users react to three different stages?
2. Related Work
3. Methods
3.1. Data Collection
3.2. Data Preprocessing
- (1)
- All reviews were converted to lower-case text.
- (2)
- The Python package NLTK [41] based standard approach was used for tokenization, lemmatization, part-of-speech tagging and stop word removal from PORs.
- (3)
- Special characters, symbols, URL’s, punctuations, numbers, words occurrence fewer than 10 times in the corpus, redundant words in the dataset such as cardinal numbers, prepositions, pronouns, etc., were removed. This content did not contribute to the review analysis.
- (4)
- Using the langid package [42], all non-English characters (non-ASCII characters) were removed from the corpus because the analysis focused on the English content of PORs.
- (5)
- All white spaces were removed whenever necessary to create tokens.
- (6)
- All repeated words were converted to their base form. For example, “sooooo happy” was converted to “so happy”.
- (7)
- All unigrams and bigrams were retained in the dataset. In this way, 2 objects, such as secondary effects, were preserved regularly in contiguous sequences. After preprocessing 152,729 PORs for 10,232 physicians met the criteria for analysis.
3.3. Topic Modeling
3.4. Topic Dynamics
3.5. Sentiment Analysis
3.6. Hybrid Sentic-LSTM
3.7. Models Classification
4. Results
4.1. Topics and their Corresponding Keywords Related to Three Stages on PRWs
4.2. Topic Trends
4.3. Public Reactions to Different Topics across Three Stages
4.4. Performance Comparison Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
2018 | 2019 | 2020 | |
---|---|---|---|
Topic 0 | |||
Doctor competence | Doctor professionalism | Clinical characterization | |
0 | Professional | Professional | Respiratory |
1 | Experienced | Knowledgeable | Pneumonia |
2 | knowledgeable | Intelligent | Novel coronavirus |
3 | Skills | Caring | Unknown cause |
4 | Intelligent | Sincerely | Person-person spread |
5 | Honest | Help | Death |
6 | Expertise | Opportunity | Case |
7 | Listen | Once again | Patient |
8 | Answering questions | Accurate diagnosis | Epidemic disease |
9 | Treatment | Serious and responsible | Leave hospital |
Topic 1 | |||
Treatment/operational process | Disease diagnosis (Cancer) | Virus transmission | |
0 | Heart | Cough | Confirmed diagnosis |
1 | Breast | Bowel | Masks |
2 | Diagnosis | Blood | Startup |
3 | Treatment | Stool | First case |
4 | Neck | Urination | First-level response |
5 | Changed the life | Testicles lumps | Goods and materials |
6 | Medical procedures | Indigestion | Nationwide |
7 | Physical therapy | Headache | Emergency |
8 | Surgery | Pain | Pneumonia |
9 | Medication | Fever | Isolation centers |
Topic 2 | |||
Disease diagnosis (heart) | Treatment experience | Travel restrictions | |
0 | Treatment | Problem diagnosis | Suspension |
1 | Surgery | Bad | Operation |
2 | Pain | Worst experience | Transport |
3 | Long time | Wrong hospital | Temporarily |
4 | Blood pressure | Painful | Strictly |
5 | Blood vessel | Long wait time | Airports |
6 | Disease diagnosis | Wrong | Boarder restrictions |
7 | Dizziness | Experience | Bus stands |
8 | Breaths shortness | Years | Long-distance |
9 | Slow heartbeat | Slow recovery | Spread |
Topic 3 | |||
Doctor’s attitude | Emergency services and trauma center | Virus symptoms | |
0 | Rude | 24 h | Fever |
1 | Depressed | Pharmacy | Cough |
2 | Long | dressing | Headache |
3 | Wait | Pain reduction | Sore throat |
4 | Questions | Medical doctor | Skin rash |
5 | Busy | Surgery | Fatigue |
6 | Careless | Reduced strain | Body aches |
7 | Less time | Trauma | Abdominal discomfort |
8 | Check | Special facilities | Stuffy nose |
9 | Ignore | Specialized equipment | Breath shortness |
Topic 4 | |||
Friendly staff | Hospital cafeteria servicescape | Activities | |
0 | Helpful | Hygienic | Home schooling |
1 | Friendly | Clean | School canceled |
2 | Amazing | Clean table | Online teaching |
3 | Wonderful | Delicious food | Week |
4 | Fantastic | Covered | Run |
5 | Bedside manner | Filter | Park |
6 | Efficient | Water | Exercise |
7 | Ease | Quick service | Diet |
8 | Professional staff | Fresh food | Job |
9 | Great staff | Comfortable environment | Coffee |
Topic 5 | |||
Communication (listen and explain) | Hospital environment | Government earlycountermeasures | |
0 | Bad | Office | WHO |
1 | Problem | Fantastic | Infectious disease |
2 | Compare | Clean | Disease management |
3 | Uncomfortable | Accessible parking | Authorities |
4 | Communication | Comfortable | Big move |
5 | Impatience | Sitting room | CDC |
6 | Worry | Nice | Testing procedures |
7 | Poor explanation | hospital cafeteria | Response |
8 | Nervous | Feel | Ventilators |
9 | Unhappy | Comfortable | Testing kits |
Topic 6 | |||
Appointment process | Chemotherapy and its side effects | Vaccine development/treatment | |
0 | Registration | Painful | Launch |
1 | Wait | Side effect | Trial |
2 | Hours | Heat | Partner |
3 | Queue | Chemotherapy | First |
4 | Online system failure | Bowel | Combat |
5 | System performance | Hair loss | Clinical |
6 | Computers | Skin | Phase |
7 | Staff absence | Nail problems | Researcher |
8 | Bad | Fatigue | Relief |
9 | Long | Nausea | Genetic |
Topic 7 | |||
Doctor value | Treatment cost | Quarantine measures and effects | |
0 | Recommend | Expenses | lockdown |
1 | Highly recommend | Money | Virus |
2 | Kind caring | Registration fee | Death |
3 | Excellent doctor | Waste money | Spread |
4 | Great doctor | Expensive | Test |
5 | Lovedr | Ineconomical | Number |
6 | God | Affordability | Follow |
7 | Trust | Difference | Report |
8 | Pleased | Insurance | State |
9 | Impressed | Credit | Country |
Topic 8 | |||
Medical examination | Medical ethics (Relational conduct) | Gratitude healthcareand reduce spread | |
0 | Operation | Knowledgeable | Thank |
1 | X-ray | Great | Together |
2 | Blood test | Care | Stay safe |
3 | Advice | Family | Help |
4 | Clear report | Manner | Maintain |
5 | Prescription | Wonderful | Protect |
6 | Inspection result | Feel | Donate |
7 | Problem | Recommend | Washingyourhands |
8 | Reexamine | Excellent | Family |
9 | Checkup | Time | Follow |
Topic 9 | |||
Patient visit process | Unfriendly and non-cooperative staff | Materials supply during COVID-19 | |
0 | Takes time | Staff | Food |
1 | Answer questions | Unfriendly | Price |
2 | Explain | Rude | Sanitizer |
3 | Listen | Front desk staff | Control |
4 | Attentive | Call | Market |
5 | Compassionate | Unhelpful | Production |
6 | Responsive | Ignore | Demand |
7 | Friendly | Loud | Supply |
8 | Cooperative | Twice | Manufacture |
9 | Help | Bother | Surgical equipment |
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Models | Accuracy | Recall |
---|---|---|
SVM | 74.32% | 69.18% |
LSTM | 81.45% | 78.25% |
Sentic-LSTM | 87.12% | 82.35% |
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Shah, A.M.; Naqvi, R.A.; Jeong, O.-R. Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets. Int. J. Environ. Res. Public Health 2021, 18, 4743. https://doi.org/10.3390/ijerph18094743
Shah AM, Naqvi RA, Jeong O-R. Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets. International Journal of Environmental Research and Public Health. 2021; 18(9):4743. https://doi.org/10.3390/ijerph18094743
Chicago/Turabian StyleShah, Adnan Muhammad, Rizwan Ali Naqvi, and Ok-Ran Jeong. 2021. "Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets" International Journal of Environmental Research and Public Health 18, no. 9: 4743. https://doi.org/10.3390/ijerph18094743