Optimizing Accuracy of Sentiment Analysis Using Deep Learning Based Classification Technique

Singh, Jaspreet; Singh, Gurvinder; Singh, Rajinder; Singh, Prithvipal

doi:10.1007/978-981-10-8527-7_43

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Removing stop words minimizes data noise and enhances the overall model accuracy in English classification [25], [26]. In Arabic, we tried three different approaches to remove stop words.…”

Section: B Removing Stop Words Manuallymentioning

confidence: 99%

Sentiment Analysis Using Stacked Gated Recurrent Unit for Arabic Tweets

Wazrah

Alhumoud²

2021

IEEE Access

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Over the last decade, the amount of Arabic content created on websites and social media has grown significantly. Opinions are shared openly and freely on social media, a process that provides a rich source for trend analyses. These analyses could be accomplished artificially by natural language processing tasks, such as sentiment analysis. Those tasks are implemented initially using machine learning. Due to its accuracy in studying unstructured data, deep learning has been increasingly used as well. The gated recurrent unit (GRU) is a promising approach in textual analysis and exhibits large morphological variations. We propose two neural models, i.e., the stacked gated recurrent unit (SGRU) and stacked bidirectional gated recurrent unit (SBi-GRU), with word embedding to mine Arabic opinions. We also propose a new way of discarding stop words using automatic sentiment refinement (ASR) instead of using manually collected stop words or using low quality available Arabic stop words' lists. The performance of our proposed models is compared with that of long short-term memory (LSTM), the support vector machine (SVM), and the most recent pretrained Arabic bidirectional encoder representations from transformers (AraBERT). In addition, we compare our models' performance to that of an ensemble architecture of the abovementioned models to find the best model architecture for Arabic natural language processing (NLP). To the best of our knowledge, no previous studies have applied either the unidirectional or bidirectional SGRU for Arabic sentiment classification. Furthermore, no ensemble models have been implemented from these architectures for the Arabic language. The results show that the six-layer SGRU stacking and five-layer SBi-GRU stacking achieve the highest accuracy and that the ensemble method outperforms all other models, with an accuracy exceeding 90%.INDEX TERMS Artificial intelligence, deep learning, natural language processing, recurrent neural networks, sentiment analysis.

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“…Removing stop words minimizes data noise and enhances the overall model accuracy in English classification [25], [26]. In Arabic, we tried three different approaches to remove stop words.…”

Section: B Removing Stop Words Manuallymentioning

confidence: 99%

Sentiment Analysis Using Stacked Gated Recurrent Unit for Arabic Tweets

Wazrah

Alhumoud²

2021

IEEE Access

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“…These technologies have provided numerous opportunities to fight such outbreaks ( Goldschmidt, 2020 ), especially determining the public’s perception of social and mass media. Multiple research studies have shown that many outbreaks could have been contained more effectively and quickly if social media data had been considered ( Singh, Singh & Bhatia, 2018 ). The COVID-19 pandemic has been a controversial topic on social media.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive sentimental analysis of tweets towards COVID-19 in Pakistan: a study on governmental preventive measures

Ali¹,

Irfan²,

Lashari³

2023

PeerJ Computer Science

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Sentiments are the key factors that lead to influence our behavior. Sentiment analysis is a technique that analyzes people’s behaviors, attitudes, and emotions toward a service, product, topic, or event. Since 2020, no country has remained untouched by COVID-19, and the governing bodies of most countries have been applying several anti-pandemic countermeasures to combat it. In this regard, it becomes tremendously important to analyze people’s sentiments when tackling infectious diseases similar to COVID-19. The countermeasures taken by any country to control the pandemic leave a direct and crucial impact on each sector of public life, and every individual reacts to them differently. It is necessary to consider these reactions to implement appropriate messaging and decisive policies. Pakistan has done enough to control this virus’s spread like every other country. This research aims to perform a sentimental analysis on the famous microblogging social platform, Twitter, to get insights into public sentiments and the attitudes displayed towards the precautionary steps taken by the Government of Pakistan in the years 2020 and 2021. These steps or countermeasures include the closure of educational institutes, suspension of flight operations, lockdown of business activities, enforcement of several standard operating procedures (SOPs), and the commencement of the vaccination program. We implemented four approaches for the analysis, including the Valence Aware Dictionary and sEntiment Reasoner (VADER), TextBlob, Flair, and Bidirectional Encoder Representations from Transformers (BERT). The first two techniques are lexicon-based. Flair is a pre-trained embedding-based approach, whereas BERT is a transformer-based model. BERT was fine-tuned and trained on a labeled dataset, achieving a validation accuracy of 92%. We observed that the polarity score kept varying from month to month in both years for all countermeasures. This score was analyzed with real-time events occurring in the country, which helped understand the public’s sentiment and led to the possible formation of a notable conclusion. All implemented approaches showed independent performances. However, we noticed from the classification results of both TextBlob and the fine-tuned BERT model that neutral sentiment was dominant in the data, followed by positive sentiment.

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Comparative Sentiment Analysis Through Traditional and Machine Learning‐Based Approach

Singh

Kaur

2021

Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithm

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Optimizing Accuracy of Sentiment Analysis Using Deep Learning Based Classification Technique

Cited by 5 publications

References 25 publications

Sentiment Analysis Using Stacked Gated Recurrent Unit for Arabic Tweets

Sentiment Analysis Using Stacked Gated Recurrent Unit for Arabic Tweets

Comprehensive sentimental analysis of tweets towards COVID-19 in Pakistan: a study on governmental preventive measures

Comparative Sentiment Analysis Through Traditional and Machine Learning‐Based Approach

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