Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media

Hsien-Tsung Chang,Ammar Amjad,Kanwar Muhammad Afaq,Lal Khan

doi:10.3390/app12052694

Hsien-Tsung Chang, Ammar Amjad + Show 2 more

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https://doi.org/10.3390/app12052694

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Abstract

Sentiment analysis (SA) has been an active research subject in the domain of natural language processing due to its important functions in interpreting people’s perspectives and drawing successful opinion-based judgments. On social media, Roman Urdu is one of the most extensively utilized dialects. Sentiment analysis of Roman Urdu is difficult due to its morphological complexities and varied dialects. The purpose of this paper is to evaluate the performance of various word embeddings for Roman Urdu and English dialects using the CNN-LSTM architecture with traditional machine learning classifiers. We introduce a novel deep learning architecture for Roman Urdu and English dialect SA based on two layers: LSTM for long-term dependency preservation and a one-layer CNN model for local feature extraction. To obtain the final classification, the feature maps learned by CNN and LSTM are fed to several machine learning classifiers. Various word embedding models support this concept. Extensive tests on four corpora show that the proposed model performs exceptionally well in Roman Urdu and English text sentiment classification, with an accuracy of 0.904, 0.841, 0.740, and 0.748 against MDPI, RUSA, RUSA-19, and UCL datasets, respectively. The results show that the SVM classifier and the Word2Vec CBOW (Continuous Bag of Words) model are more beneficial options for Roman Urdu sentiment analysis, but that BERT word embedding, two-layer LSTM, and SVM as a classifier function are more suitable options for English language sentiment analysis. The suggested model outperforms existing well-known advanced models on relevant corpora, improving the accuracy by up to 5%.

Highlights

Introduction published maps and institutional affilAs a result of the rise of low-cost mobile devices and ultra-fast Internet connection, users have recently been inspired to submit a variety of data on social networking sites such as Twitter, Facebook, and YouTube
The basic CNN-LSTM design has been widely employed in previous studies [53–55], the CNN-LSTM model we propose is unique for the following reasons: (1) in comparison to previous studies, our suggested model includes one additional layer of LSTM to improve the performance; (2) while the majority of previous studies used softmax as a classification function, we included traditional machine learning models such as naive Bayes (NB), DT, KNN, RF, and support vector machine (SVM) in our suggested model
The proposed and existing models further differ in that we study the CNN-LSTM architecture using a variety of word embedding models including fixed (GloVe, Word2Vec), pre-trained (FastText), and context-based (BERT)

Summary

Introduction

As a result of the rise of low-cost mobile devices and ultra-fast Internet connection, users have recently been inspired to submit a variety of data on social networking sites such as Twitter, Facebook, and YouTube. User input on a variety of things, as well as on people’s thoughts regarding services, online learning, and other issues, is included in these data [1,2]. As the use of social networking platforms expands, users are encouraged to share their opinions and emotions, and to participate in a variety of discussion groups [3–5]. Sentiment analysis (SA) is critical for comprehending people’s actions [6–8]. Most businesses and governments are interested in obtaining important information from user comments, such as the emotions and feelings that underpin client opinions [10–13]. Natural language processing (NLP), data mining, text analysis, machine learning, and deep learning approaches are used to analyze the iations

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