- What are the most discussed topics posted by users in a customer service platform regarding a transport company from Twitter?
- How do different topic modeling and clustering technologies compare in terms of performance?
- What are the areas from transport and customer services where user satisfaction needs to be improved?
2. Related Work
3. Materials and Methods
3.1. Data Collection
- They must be written in English since our goal is to address the English- speaking community.
- Tweets posted by the customer service @Uber_Support were eliminated since our achievement is to analyze customers’ demands and issues with the brand.
- Duplicated tweets were eliminated.
- Concerning spam detection, some of the most popular methods for generating fundamental truth are physical examination and filtering of blacklists . The use of machine learning methods for detecting spammers  is out of the scope of this work and left as an interesting research line. In our case, we have used a physical examination of the dataset by selecting the first 20 user accounts that posted the highest volume of tweets in the dataset. Then, if one of those users was a spammer, the whole account was eliminated from the dataset. In our particular case, we filtered 4 of these 20 accounts since we detected them as spammers. This analysis was fundamentally performed to reduce the creation of false topics in the topic modeling approach (Section 3.3).
3.2. Data Pre-Processing
3.3. LDA for Topic Modeling
3.4. Determining T Optimal Number of Topics
3.5. Document Clustering
3.5.1. Clustering Algorithm: K-Means Description
3.5.2. Genetic Algorithm
3.5.3. Local Convergence Algorithm
3.6. Sentiment and Emotion Analysis
- For the task of sentiment extraction, we have used the Senpy  framework that provides a simple interface to a wide number of Sentiment analysis services. In particular, we have used the plugin Sentiment 140 since it can be executed locally on our server.
- With respect to the methodology used for the emotion extraction, the framework Rapidminer  combined with the MeaningCloud  commercial platform was implemented through the Deep Categorization API. Specifically, the Emotion Recognition categorization model was used. Besides, this analysis resulted in the individual evaluation of each tweet which was classified as a mixture of trust, joy, sadness, anger, disgust, anticipation, fear, and surprise emotions.
4.1. Data Collection and Global Analysis
4.2. Topic Modeling Performances and Results
4.3. Clustering Performances and Results
4.4. Sentiment and Emotion Analysis of Each Topic
5. Conclusions and Outlook
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
|DEAP||Distributed Evolutionary Algorithms in Python|
|LDA||Latent Dirichlet Allocation|
|LSI||Latent Semantic Analysis|
|MCMC||Markov Chain Monte Carlo|
|NLP||Natural Language Processing|
|NLTK||Natural Language Toolkit|
|NMF||Non-negative Matrix Factorization|
|SLSQP||Sequential Least-Squares Programming|
|TWINT||Twitter Intelligence Tool|
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|Topic Modeling Evaluation|
|Topic Name||Wordcloud||Topic Description|
|Uber account/Uber app||Customers reporting issues when logging into Uber app or validating their accounts.|
|Uber drivers||Customers reporting issues or giving their opinion about Uber drivers. This topic also includes Uber drivers asking for support about their licences and regulation.|
|Money and payments||Users contacting with Uber Support platform due to money issues and wrong payments on their accounts.|
|Uber Eats||Clients contacting with Uber Support to resolve issues related with Uber Eats platform.|
|Opinions about Customer Service||Tweets sharing the experience about contacting Customer Service or asking for additional support or refunds.|
|Time and cancellation||Time related issues, including cancellation of the trip by the driver at the last minute and longer than expected waiting time to the driver to arrive.|
|Contact with Uber Support||Clients or drivers contacting directly with Uber Support to provide alternative ways of contact (email, telephone, direct message...) or answering previous conversations.|
|Topic Name||Predominant Component||No. Tweets (%)|
|Kmeans||Hybridized GA||Kmeans||Hybridized GA|
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