- Tytuł:
- Cloud-based sentiment analysis for measuring customer satisfaction in the Moroccan banking sector using Naïve Bayes and Stanford NLP
- Autorzy:
-
Riadsolh, Anouar
Lasri, Imane
ElBelkacemi, Mourad - Powiązania:
- https://bibliotekanauki.pl/articles/2141901.pdf
- Data publikacji:
- 2020
- Wydawca:
- Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
- Tematy:
-
Big Data processing
Apache Spark
Apache Kafka
real-time text processing
sentiment analysis
Stanford core NLP
Naïve Bayes classifier - Opis:
- In a world where every day we produce 2.5 quintillion bytes of data, sentiment analysis has been a key for making sense of that data. However, to process huge text data in real-time requires building a data processing pipeline in order to minimize the latency to process data streams. In this paper, we explain and evaluate our proposed real-time customer’ sentiment analysis pipeline on the Moroccan banking sector through data from the web and social network using open-source big data tools such as data ingestion using Apache Kafka, In-memory data processing using Apache Spark, Apache HBase for storing tweets and the satisfaction indicator, and ElasticSearch and Kibana for visualization then NodeJS for building a web application. The performance evaluation of Naïve Bayesian model show that for French Tweets the accuracy has reached 76.19% while for English Tweets the result was unsatisfactory and the resulting accuracy is 56%. To remedy this problem, we used the Stanford core NLP which, for English Tweets, reaches a precision of 80.7%.
- Źródło:
-
Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 4; 64-71
1897-8649
2080-2145 - Pojawia się w:
- Journal of Automation Mobile Robotics and Intelligent Systems
- Dostawca treści:
- Biblioteka Nauki
Artykuł