No 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.

Product reviews are the individual’s opinions, judgement or belief about a certain product or service provided by certain companies. Such reviews serve as guides for these companies to plan and monitor their business ventures in terms of increasing productivity or enhancing their product/service qualities. Product reviews can also increase business profits by convincing future customers about the products which they have interest in. In the mobile application marketplace such as Google Playstore, reviews and star ratings are used as indicators of the application quality. However, among all these reviews, hereby also known as opinions, spams also exist, to disrupt the online business balance. Previous studies used the time series and neural network approach (which require a lot of computational power) to detect these opinion spams. However, the detection performance can be restricted in terms of accuracy because the approach focusses on basic, discrete and document level features only thereby, projecting little statistical relationships. Aiming to improve the detection of opinion spams in mobile application marketplace, this study proposes using statistical based features that are modelled through the supervised boosting approach such as the Extreme Gradient Boost (XGBoost) and the Generalized Boosted Regression Model (GBM) to evaluate two multilingual datasets (i.e. English and Malay language). From the evaluation done, it was found that the XGBoost is most suitable for detecting opinion spams in the English dataset while the GBM Gaussian is most suitable for the Malay dataset. The comparative analysis also indicates that the implementation of the proposed statistical based features had achieved a detection accuracy rate of 87.43 per cent on the English dataset and 86.13 per cent on the Malay dataset.