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

Identyfikatory

DOI

10.14313/JAMRIS/4-2020/47

• Języki publikacji: EN

Abstrakty

EN

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%.

Słowa kluczowe

EN

Big Data processing; Apache Spark; Apache Kafka; real-time text processing; sentiment analysis; Stanford core NLP; Naïve Bayes classifier

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2020
• Tom: Vol. 14, No. 4
• Strony: 64--71
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Riadsolh Anouar

• Laboratory of Conception and Systems, Faculty of Sciences, Mohammed V University in Rabat, Morocco

autor

Lasri Imane

• Laboratory of Conception and Systems, Faculty of Sciences, Mohammed V University in Rabat, Morocco

autor

ElBelkacemi Mourad

• Laboratory of Conception and Systems, Faculty of Sciences, Mohammed V University in Rabat, Morocco

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).