Recognition of Ironic Sentences in Twitter using Attention-Based LSTM

Martini, Andrianarisoa Tojo; Farrukh, Makhmudov; Ge, Hongwei

doi:10.14569/ijacsa.2018.090802

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Rudkowski et al [20] showed the effectiveness of embedding words into mood analysis. Martini et al [21] suggest a method for detecting ironic proposals on Twitter using an attention-based LSTM. Tadesse et al [22] used a joint LSTM-CNN model to detect suicidal thoughts online.…”

Section: Related Workmentioning

confidence: 99%

PDHS: Pattern-Based Deep Hate Speech Detection With Improved Tweet Representation

Sharmila¹,

Anbananthen

Chelliah³

et al. 2022

IEEE Access

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Automatic hate speech identification in unstructured Twitter is significantly more difficult to analyze, posing a significant challenge. Existing models heavily depend on feature engineering, which increases the time complexity of detecting hate speech. This work aims to classify and detect hate speech using a linguistic pattern-based approach as pre-trained transformer language models. As a result, a novel Pattern-based Deep Hate Speech (PDHS) detection model was proposed to detect the presence of hate speech using a cross-attention encoder with a dual-level attention mechanism. Instead of concatenating the features, our model computes dot product attention for better representation by reducing the irrelevant features. The first level of Attention is extracting aspect terms using predefined parts-of-speech tagging. The second level of Attention is extracting the sentiment polarity to form a pattern. Our proposed model trains the extracted patterns with term frequency, parts-of-speech tag, and Sentiment Scores. The experimental results on Twitter Dataset can learn effective features to enhance the performance with minimum training time and attained 88%F1Score.

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Section: Related Workmentioning

confidence: 99%

PDHS: Pattern-Based Deep Hate Speech Detection With Improved Tweet Representation

Sharmila¹,

Anbananthen

Chelliah³

et al. 2022

IEEE Access

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“…Although there are many positive words and praise words at the end of the sentence, this sentence is a harsh sentence that shows the disappointment of a buyer who orders an item but gets a different piece from what he desired. Irony can be perceived as a result of incongruity between the context and the statement [2], [3], [4]. Meanwhile, sarcasm is also found as a result of incongruity between the context and the statement but more often intended to mention, mock or offend someone, a product or an institution [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Challenges of Sarcasm Detection for Social Network : A Literature Review

et al. 2020

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Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance.

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