Halal Products on Twitter: Data Extraction and Sentiment Analysis Using Stack of Deep Learning Algorithms

Abstract

Twitter is a leading platform among social media networks. It allows microblogging of up to 140 characters for a single post. Owing to this characteristic, it is popular among users. People tweet about various topics from daily life events to major incidents. Given the influence of this social media platform, the analysis of Twitter contents has become a research area as it gives us useful insights on a topic. Hence, this paper will describe how Twitter data are extracted, and the sentiment of the tweets on a particular topic is calculated. This paper focusses on tweets of two halal products, i.e., halal tourism and halal cosmetics. Twitter data (over a 10-year span) were extracted using the Twitter search function, and an algorithm was used to filter the data. Then, an experiment was conducted to calculate and analyze the tweets' sentiment using deep learning algorithms. In addition, convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN) were utilized to improve the accuracy and construct prediction models. Among the results, it was found that the Word2vec feature extraction method combined with a stack of the CNN and LSTM algorithms achieved the highest accuracy of 93.78%. INDEX TERMS Twitter, algorithm, convolutional neural networks (CNN), long short-term memory (LSTM), recurrent neural networks, Halal tourism, Halal cosmetics, sentiment analysis.

Automatic Event Geo-Location in Twitter

“…In the literature, several works have been developed to manage Twitter data [6] [7], but the most part of them focuses on solving the event detection problem [1] [18], whereas, to the best of our knowledge, only recently researches are devoted to geo-locate Twitter events [2].…”

“…Once computed the set of candidate location names, it uses the Google Maps API 6 to verify whether the candidate location names correspond to effective locations. In particular, for each candidate location name, the Google Maps API verifies whether there exists an exact, partial or null match with real locations.…”

Hybrid CNN Classification for Sentiment Analysis under Deep Learning

“…In a research [27] that focused on tweets of two halal products, i.e., halal tourism and halal cosmetics, twitter data over a span of 10 years were extracted using the twitter search function, and an algorithm to filter the data. Later, DL algorithm was used to calculate and analyze the tweet's sentiments.…”

Sentiment Analysis using Artificial Neural Network

“…The outcomes concluded that the introduced approaches were evaluated and demonstrated that the approaches outperformed other baselines where the performance of task of aspect OTE was enhanced with 39% while the second task had shown the enhancement of 6% in the performance. Then, in 2019, stack of deep learning algorithms was proffered by Feizollah et al [23] for analyzing the sentiments. The author performed analysis on the tweets on Halal products which were, Halal cosmetics and Halal tourism.…”