Fintech Facebook Sentiment Analysis
•
1 like•249 views
With the heavy use of social media by most Filipinos to review products and services, Team Unicorn was sponsored by TELUS International Philippines to study the reactions to different posts to help companies improve information dissemination and customer experience. Jen Marquez, Cherry Bondal and Leia made an NLP study on a FinTech company's publicly available social media comments. They used SVM for sentiment analysis and Random Forest classifier for their final model which can predict the reactions of a social media post with 65% accuracy. Want to hire a #datascientist? http://bit.ly/hireFTW
Recommended
More Related Content
Similar to Fintech Facebook Sentiment Analysis
Similar to Fintech Facebook Sentiment Analysis (20)
More from For The Women Foundation
More from For The Women Foundation (20)
Recently uploaded
Recently uploaded (20)
Fintech Facebook Sentiment Analysis
- 1. SOCIAL MEDIA ANALYSIS TEAMUNICORN An NLP Study on a Fintech Company
- 2. FTW’s TEAM UNICORN Industrial Engineering Graduate Doctor of Veterinary Medicine Graduate Computer Engineering Graduate CHERRY JEAN BONDAL JENNELYN MARQUEZ LEA RONQUILLO
- 3. Source: https://www.globenewswire.com/news-release/2019/03/25/1759927/0/en/United-States-Prepaid-Cards-Business-and-Investment-Opportunities-Report-2019.html A CLOSER LOOK AT THE MARKET $561B2019 $897B 2023
- 4. Source: https://www.shopify.com/enterprise/global-ecommerce-statistics ECOMMERCE GROWTH RATE United States is among the top with the highest five-year growth rate The US ecommerce market drives FinTech industry growth Country 2018 2023 5-year growth China $636.1B $1,086.1B 70.7% United States $504.6B $735.4B 45.7% France $49.4B $71.9B 45.6% Australia $18.6B $26.9B 44.6% Russia $17.2B 24.8B 44.2%
- 5. Source: https://www.statista.com/statistics/816735/customer-churn-rate-by-industry-us/ CUSTOMER CHURN RATE Financial Industry is among the top 5 with the highest churn rate 1 out of 4 FinTech customers are at risk of going over to rival companies
- 6. WHAT MAKES A BUSINESS SUCCESSFUL?
- 7. WHAT DO THE CUSTOMERS SAY?
- 8. … AND HOW DOES THE COMPANY ENGAGE WITH CUSTOMERS?
- 9. WHAT WE WANT TO KNOW: How can we improve customer experience? What type of activities and content promote positive engagement? What drives negative sentiments?
- 10. PROJECT OVERVIEW Prepare the Data Data Cleaning Feature Engineering Exploring Data Build the Model Predicting Sentiment Predicting Post Reactions Derive Insights Social Media Analysis Recommendations
- 11. TextBlob - 49% HOW DID WE DO IT? Noise Removal Stemming Lemmatization DATA CLEANING PRE-PROCESSING SENTIMENT MODEL VaderSentiment - 54% Naive Bayes - 75% SVM-C - 82% Topic Modeling Data Normalization EXPLORE DATA PREDICTIVE MODEL Random Forest Classifier - 65% SVM-C - 61% Naive Bayes - 60% RAW DATA CLEANED DATA INSIGHTS Social Media Analysis Recommendations
- 12. RAW DATA Noise Removal Removed null values, rows Stemming running -> run Lemmatization better -> good DATA CLEANING PRE-PROCESSING Topic Modeling Latent Dirichlet Allocation Data Normalization StandardScaler MinMaxScaler PREPARE DATA
- 13. BUILD MODEL CLEANED DATA EXPLORE DATA TextBlob - 49% VaderSentiment - 54% Naive Bayes - 75% SVM-C - 82% SENTIMENT MODEL PREDICTIVE MODEL Random Forest Classifier - 65% SVM-C - 61% Naive Bayes - 60% RESULTS
- 14. DERIVE INSIGHTS RESULTS INSIGHTS Social Media Analysis Recommendations
- 15. HOW DO OUR CUSTOMERS FEEL? There is only 17% positive sentiments.
- 22. POST REACTION PREDICTIVE MODEL Predicts the post reaction it will gain based on the content, time, type of post, and category Time Type Category Topic 1 . . . Topic N RANDOM FOREST CLASSIFIER INPUT PREPARE DATA FEATURES PREDICTIVE MODEL PRE PROCESSING OUTPUT Post Reaction: ● Positive ● Neutral ● Negative
- 23. CREATE A SUPERVISED COMMUNITY HELP CENTER Customers can interact and help each other. CONVEY THROUGH PHOTOS Customers are more likely to react or respond to posts with photos. COMMUNICATE STRATEGICALLY Customers are more likely to participate in the conversation outside of their office hours. RECOMMENDATIONS