@inproceedings{6dcba201c47844ffb18bf9b56efe85af,
title = "Investigating Deep Learning Word2vec Model for Sentiment Analysis in Arabic and English languages for User's reviews",
abstract = "In this paper, we explore natural language processing (NLP) methods to perform sentiment analysis or opinion mining. In addition, we show an application on English and Arabic sentiment analysis by implementing sentiment classification for three datasets which are Booking hotel dataset, Food fine Amazon dataset and Arabic movie review dataset. We applied Word2Vec model followed by Random Forest classifier (RF) for Arabic movie dataset. The results show that the Word2Vec model shows highly effective performance in sentiment analysis for English language datasets but it does not work for Arabic language as Arabic language need different mechanism. ",
keywords = "Arabic language, NLP, Sentiment analysis, Text Mining, Word2Vec",
author = "Maram Almaghrabi and Girija Chetty",
year = "2019",
month = dec,
doi = "10.1109/csde48274.2019.9162391",
language = "English",
isbn = "9781728163048",
series = "2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2019",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "1--6",
editor = "Takeshi Washio",
booktitle = "Proceedings of the 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)",
address = "United States",
note = "2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2019 ; Conference date: 09-12-2019 Through 11-12-2019",
}