Detection of Disaster-Affected Cultural Heritage Sites from Social Media Images Using Deep Learning Techniques

Sentiments is the term of opinion or views about any topic expressed by the people through a source of communication. Nowadays social media is an effective platform for people to communicate and it generates huge amount of unstructured details every day. It is essential for any business organization in the current era to process and analyse the sentiments by using machine learning and Natural Language Processing (NLP) strategies. Even though in recent times the deep learning strategies are becoming more familiar due to higher capabilities of performance. This paper represents an empirical study of an application of deep learning techniques in Sentiment Analysis (SA) for sarcastic messages and their increasing scope in real time. Taxonomy of the sentiment analysis in recent times and their key terms are also been highlighted in the manuscript. The survey concludes the recent datasets considered, their key contributions and the performance of deep learning model applied with its primary purpose like sarcasm detection in order to describe the efficiency of deep learning frameworks in the domain of sentimental analysis.

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Comparative Analysis of Deep Learning Techniques for the Classification of Hate Speech

NIGERIAN ANNALS OF PURE AND APPLIED SCIENCES ◽

10.46912/napas.227 ◽

2021 ◽

Vol 4 (1) ◽

pp. 121-128

Author(s):

A Iorliam ◽

S Agber ◽

MP Dzungwe ◽

DK Kwaghtyo ◽

S Bum

Keyword(s):

Neural Network ◽

Social Media ◽

Deep Learning ◽

Hate Speech ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Learning Techniques ◽

Or Groups ◽

Long Short Term Memory

Social media provides opportunities for individuals to anonymously communicate and express hateful feelings and opinions at the comfort of their rooms. This anonymity has become a shield for many individuals or groups who use social media to express deep hatred for other individuals or groups, tribes or race, religion, gender, as well as belief systems. In this study, a comparative analysis is performed using Long Short-Term Memory and Convolutional Neural Network deep learning techniques for Hate Speech classification. This analysis demonstrates that the Long Short-Term Memory classifier achieved an accuracy of 92.47%, while the Convolutional Neural Network classifier achieved an accuracy of 92.74%. These results showed that deep learning techniques can effectively classify hate speech from normal speech.

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Comparative Analysis of Deep Learning Techniques to detect Online Public Shaming

ITM Web of Conferences ◽

10.1051/itmconf/20214003030 ◽

2021 ◽

Vol 40 ◽

pp. 03030

Author(s):

Mehdi Surani ◽

Ramchandra Mangrulkar

Keyword(s):

Social Media ◽

Deep Learning ◽

Freedom Of Speech ◽

Learning Approaches ◽

Learning Models ◽

Media Usage ◽

The Past ◽

Learning Techniques ◽

Social Media Platforms ◽

Machine Learning Models

Over the past years the exponential growth of social media usage has given the power to every individual to share their opinions freely. This has led to numerous threats allowing users to exploit their freedom of speech, thus spreading hateful comments, using abusive language, carrying out personal attacks, and sometimes even to the extent of cyberbullying. However, determining abusive content is not a difficult task and many social media platforms have solutions available already but at the same time, many are searching for more efficient ways and solutions to overcome this issue. Traditional models explore machine learning models to identify negative content posted on social media. Shaming categories are explored, and content is put in place according to the label. Such categorization is easy to detect as the contextual language used is direct. However, the use of irony to mock or convey contempt is also a part of public shaming and must be considered while categorizing the shaming labels. In this research paper, various shaming types, namely toxic, severe toxic, obscene, threat, insult, identity hate, and sarcasm are predicted using deep learning approaches like CNN and LSTM. These models have been studied along with traditional models to determine which model gives the most accurate results.

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Deep Learning for emotion analysis in Arabic tweets

10.21203/rs.3.rs-631537/v1 ◽

2021 ◽

Author(s):

ENAS ABDEL HAKIM KHALIL ◽

Enas .M.F. El Houby ◽

Hoda .k. Mohamed

Keyword(s):

Social Media ◽

Deep Learning ◽

Short Term Memory ◽

Model Performance ◽

Support Vector ◽

Emotion Classification ◽

Network Layers ◽

Learning Techniques ◽

Comparable Performance ◽

Fully Connected

Abstract Expressing our emotions using text and emojis expressions became widespread through social media such as Facebook, Instagram, Twitter, Weibo, and LinkedIn. Nowadays, both organizations and individuals are interested in using social media to analyze people's opinions and extract sentiments and emotions. We proposed a model for multilabel emotion classification, using a bidirectional Long Short-term Memory BiLSTM deep network. It is evaluated on the Arabic tweets' dataset provided by SemEval 2018 for the E-c task. Several preprocessing steps, including ARLSTEM with some modifications, replacing emojis with corresponding text meaning from a manually built lexicon, and feature vector representation using Aravec word embedding is applied. The novelty in our research that it examines the effect of hyperparameter tuning on model performance, and it uses BiLSTM in all of its deep neural network layers. The proposed model achieves a comparable performance with state-of-the-art models using different machine learning and deep learning techniques. The system achieves about 9% enhancement in validation accuracy compared with the last best model in the same task using Support Vector classifier SVC; it outperforms the other deep neural networks (UNCCTeam) based on fully connected layers in micro F1 metric of about 4.4%.

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