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%.
Highlights
Sentiment analysis is a branch of natural language processing that analyzes text using machine learning algorithms
ARCHITECTURE This work aims at performing a sentiment analysis on two halal domains, namely halal tourism and halal cosmetics
The results show that the average sentiment score for tourism is ranged between 0.3136 by word2vec_cnn_birnn_bilstm and 0.5436 by the word2vec_lstm algorithm
Summary
Sentiment analysis is a branch of natural language processing that analyzes text using machine learning algorithms. This method has attracted the attention of many developers and researchers. They identified polarity of text using sentiment analysis accurately. The Twitter platform has been a key pillar of social networks. It is a podium for politicians, scientists, celebrities, etc. To express their views on a topic. As these sites are always accessible without the limitations of time and location, users regularly create contents ranging from daily life events to serious incidents. The influence of social media, in general, The associate editor coordinating the review of this manuscript and approving it for publication was Xi Peng
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