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TextMining.pptx
- 1. Analysing Digital Banking Reviews Using Text Mining IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2020 Li Chen Cheng and Legaspi Rhea Shannayne Department of Information and Finance Management National Taipei University of Technology Taipei, Taiwan
- 2. OUTLINE PROBLEM Prioritize features Summary METHODOLOGY How to find Hidden patterns in huge data INTRODUCTION Digital Banking Why text Mining ? 01 02 04 Latent Allocation DLA Association mining rules SOLUTION 03 CONCLUSION 06 2 solution implementation DEMO CODE 05
- 4. 4
- 5. WhyTextMiNING? 01. The feedback provided by the users usually reflect their satisfaction on the application , and Satisfaction has a direct impact on the user's loyalty (Wang et al, 2018). Critical for companies to understand their market
- 6. PROBLEM 02 6
- 7. Unstructured nature Huge number of user reviews Some may not even provide meaningful insights. 7 Researcher stated that the emotional sentiment of the user is only weakly correlated to the app rating(2017).
- 9. Reveal which areas the digital banking application can further optimize for customer satisfaction and retention. Maingoal Consumer reviews ?
- 10. This Paper 10 Goal Identify which features need to be prioritized and improved upon, so they will be able to focus their efforts and better target their strategies to meet customers' expectations. What are users most concerned about? Which features are viewed positively and which are viewed negatively? Research Questions Not only about the satisfaction but also what topic should the Digital bank improve !
- 11. PROPOSED METHODOLOGY 03 11 HOW , WHAT THEY USED TO IMPLEMENT THE SOLUTION
- 12. Topic Modeling 12 How can we label such a huge amount of data if not manually? Problem Analyze a huge amount of data, cluster them into similar groups, and label each group. Clustering documents into topics consists of a cluster of words that often occur together It is based on the assumption that: ● A document is composed of several topics ● A topic is composed of a collection of words
- 13. Topic Modeling : Latent Dirichlet Allocation (LDA) 13 ● Latent Dirichlet Allocation is a probabilistic method for Topic Modelling ● We have to choose the number of topics k that we want to ‘discover’ in our corpus ● The results then find these hidden topics, and give us the words that make up each topic, in the form of a probability distribution over the vocabulary for each topic.
- 14. 14 Comment 1: "I am very happy with the bank's mobile app, it is very convenient and easy to use." Comment 2: "I am very disappointed with the bank's customer service, they are not helpful at all." Comment 3: "I find the bank's interest rates to be very competitive and fair." . . . Topic 3: Interest rates ["mobile app", "convenient", "user-friendly", "easy to use", etc] Topic 1: Mobile app Topic 2: Customer service Topic 1 % Topic 1 % Topic 2
- 15. Paper extracted Topics 15 ["customer", "service", "poor","waste", "phone", "email" ] ● reveals that the usual contact methods of the users are through phone and email. ● reveals that the application's customer service is currently unsatisfactory.
- 16. Association Analysis 16 Technique that is used to uncover the underlying relationships and patterns in a dataset. It is used to identify the items or attributes that are frequently co-occurring or associated in some way.
- 17. 17
- 18. SUMMARY 18 1-First, this research applied LDA to pre-processed text reviews to obtain the relevant topics . 2- Second, keywords are extracted from the text based on their count frequency and labelled into specific digital banking features. 3- The reviews are then divided into two clusters based on the current review rating ("positive" and "negative") and association rule mining is applied to determine the relationship between the above-mentioned features and the given rating. LDA +Association mining uncover the underlying topics present in a corpus find patterns and relationships within the topics identified by LDA.
- 19. Conclusion 05 19
- 20. Thanks! Do you have any questions? 20
Editor's Notes
- presented at the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) , and it is a conference that focuses on research related to social networks analysis and mining.
- igital banks are banks that operate primarily online, offering banking services to customers through digital channels such as mobile apps, online portals They also often have more flexible account opening processes, such as the ability to open an account entirely online
- With the advent of the internet and social media, information has become more widespread and easier to access.
- Now what are the problem with customer reviews ?
- Generate base lear which proves that the app rating is not an accurate representation of the customers' sentiment.
- obtain valuable knowledge that was initially unrevealed in online reviews.
- based on
- so it’s not only about sentiment analysis , it’s take the reviews and based on them they extract what topic are users concerned about (is the online accounting , the mony ..) and are they satisfied with it or not. okay they will understand the sentiment (the satisfaction ) but what topic should the digal bank improve ?
- obtain valuable knowledge that was initially unrevealed in online reviews.
- https://moj-analytical-services.github.io/NLP-guidance/LDA.html https://aiinarabic.com/latent-dirichlet-allocation-algorithm-using-python/ topic can be described as a collection of words, like [‘ball’, ‘cat’, ‘house’] and [‘airplane’, ‘clouds’], but in practice, what an algorithm does is assign each word in our vocabulary a ‘participation’ value in a given topic. The words with the highest values can be considered as the true participants of a topic.
- https://moj-analytical-services.github.io/NLP-guidance/LDA.html https://aiinarabic.com/latent-dirichlet-allocation-algorithm-using-python/ words that present each topic
- https://www.linkedin.com/pulse/topic-modeling-using-latent-dirichlet-allocation-saket-khare-frm Each topic would be represented by a set of words that are likely to be semantically related. represented by words such as "mobile app", "convenient", "user-friendly", "easy to use", etc. The model assigns a probability to each word within a topic and also assigns a probability to each topic in the document.
- LDA to, and then use association rule mining to find patterns and relationships within the topics identified by LDA.