Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

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Artificial Intelligence-enabled analysis of social media data to understand public perceptions of COVID-19 contact tracing apps (Preprint)

Journal of Medical Internet Research ◽

10.2196/26618 ◽

2020 ◽

Author(s):

Kathrin Cresswell ◽

Ahsen Tahir ◽

Zakariya Sheikh ◽

Zain Hussain ◽

Andrés Domínguez Hernández ◽

...

Keyword(s):

Artificial Intelligence ◽

Social Media ◽

Public Perceptions ◽

Contact Tracing ◽

Social Media Data ◽

Media Data

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Product Review Analysis Using Social Media Data Based on Sentiment Analysis

Innovations in Computer Science and Engineering - Lecture Notes in Networks and Systems ◽

10.1007/978-981-15-2043-3_57 ◽

2020 ◽

pp. 527-533

Author(s):

S. M. Mazharul Hoque Chowdhury ◽

Sheikh Abujar ◽

Ohidujjaman ◽

Khalid Been Md. Badruzzaman ◽

Syed Akhter Hossain

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Product Review ◽

Social Media Data ◽

Review Analysis ◽

Media Data

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Employing the Sentiment Analysis Tool in NVivo 11 Plus on Social Media Data

Social Media Listening and Monitoring for Business Applications - Advances in E-Business Research ◽

10.4018/978-1-5225-0846-5.ch010 ◽

2017 ◽

pp. 175-244

Author(s):

Shalin Hai-Jew

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Analysis Tool ◽

Attitudes And Beliefs ◽

Social Media Data ◽

Public Issues ◽

Public Events ◽

Brand Reputation ◽

Text Corpora ◽

Media Data

Sentiment analysis has been used to assess people's feelings, attitudes, and beliefs, ranging from positive to negative, on a variety of phenomena. Several new autocoding features in NVivo 11 Plus enable the capturing of sentiment analysis and extraction of themes from text datasets. This chapter describes eight scenarios in which these tools may be applied to social media data, to (1) profile egos and entities, (2) analyze groups, (3) explore metadata for latent public conceptualizations, (4) examine trending public issues, (5) delve into public concepts, (6) observe public events, (7) analyze brand reputation, and (8) inspect text corpora for emergent insights.

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Aspect-level Sentiment Analysis for Social Media Data in the Political Domain using Hierarchical Attention and Position Embeddings

2020 International Conference on Data Science and Its Applications (ICoDSA) ◽

10.1109/icodsa50139.2020.9212883 ◽

2020 ◽

Author(s):

Renny Pradina Kusumawardani ◽

Muhammad Wildan Maulidani

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

The Political ◽

Social Media Data ◽

Media Data

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Sentiment Analysis Methods for HPV Vaccines Related Tweets Based on Transfer Learning

Healthcare ◽

10.3390/healthcare8030307 ◽

2020 ◽

Vol 8 (3) ◽

pp. 307

Author(s):

Li Zhang ◽

Haimeng Fan ◽

Chengxia Peng ◽

Guozheng Rao ◽

Qing Cong

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Transfer Learning ◽

Hpv Vaccination ◽

Experimental Results ◽

Fine Tuning ◽

Hpv Vaccines ◽

Social Media Data ◽

Public Sentiment ◽

Media Data

The widespread use of social media provides a large amount of data for public sentiment analysis. Based on social media data, researchers can study public opinions on human papillomavirus (HPV) vaccines on social media using machine learning-based approaches that will help us understand the reasons behind the low vaccine coverage. However, social media data is usually unannotated, and data annotation is costly. The lack of an abundant annotated dataset limits the application of deep learning methods in effectively training models. To tackle this problem, we propose three transfer learning approaches to analyze the public sentiment on HPV vaccines on Twitter. One was transferring static embeddings and embeddings from language models (ELMo) and then processing by bidirectional gated recurrent unit with attention (BiGRU-Att), called DWE-BiGRU-Att. The others were fine-tuning pre-trained models with limited annotated data, called fine-tuning generative pre-training (GPT) and fine-tuning bidirectional encoder representations from transformers (BERT). The fine-tuned GPT model was built on the pre-trained generative pre-training (GPT) model. The fine-tuned BERT model was constructed with BERT model. The experimental results on the HPV dataset demonstrated the efficacy of the three methods in the sentiment analysis of the HPV vaccination task. The experimental results on the HPV dataset demonstrated the efficacy of the methods in the sentiment analysis of the HPV vaccination task. The fine-tuned BERT model outperforms all other methods. It can help to find strategies to improve vaccine uptake.

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