Temat: Bayes classifier - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Computerised Recommendations On E-Transaction Finalisation By Means Of Machine Learning
Autorzy:: Budnikas, Germanas
Powiązania:: https://bibliotekanauki.pl/articles/466046.pdf
Data publikacji:: 2015
Wydawca:: Główny Urząd Statystyczny
Tematy:: online behaviour
Google Analytics
Naïve Bayes classifier
artificial neural network
Opis:: Nowadays a vast majority of businesses are supported or executed online. Website-to-user interaction is extremely important and user browsing activity on a website is becoming important to analyse. This paper is devoted to the research on user online behaviour and making computerised advices. Several problems and their solutions are discussed: to know user behaviour online pattern with respect to business objectives and estimate a possible highest impact on user online activity. The approach suggested in the paper uses the following techniques: Business Process Modelling for formalisation of user online activity; Google Analytics tracking code function for gathering statistical data about user online activities; Naïve Bayes classifier and a feedforward neural network for a classification of online patterns of user behaviour as well as for an estimation of a website component that has the highest impact on a fulfilment of business objective by a user and which will be advised to be looked at. The technique is illustrated by an example.
Źródło:: Statistics in Transition new series; 2015, 16, 2; 309-322
1234-7655
Pojawia się w:: Statistics in Transition new series
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 2.

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

Zmień widok

na półce

Informacja

Wyszukujesz frazę "Bayes classifier" wg kryterium: Temat

Źródło danych

Dostawca treści

Kolekcja

Rok wydania

Wydawca

Temat

Autor

Typ dokumentu

Język