Sarcasm Analysis on Twitter Data Using Machine Learning Approaches

Bharti, Santosh Kumar; Pradhan, Ramkrushna; Babu, Korra Sathya; Jena, Sanjay Kumar

doi:10.1007/978-3-319-53420-6_3

Cited by 25 publications

(8 citation statements)

References 17 publications

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“…However, it also opens the door for unlawful activities which harm society, business markets, healthcare systems, etc. where incorrect or misleading information is intentionally or unintentionally spread (Bharti et al 2017;Sun et al 2018;Gao and Liu 2014). Therefore, SN has attracted a lot of attention and is considered to be a developing interdisciplinary research area that aims to analyze, combine, explore, and adjust techniques to investigate SN data globally.…”

Section: Introductionmentioning

confidence: 99%

“…Misinformation is inaccurate information which is created to misguide the readers (Fernandez and Alani 2018;Zhang et al 2018a). There are numerous terms related to misinformation including fake news, rumors, spam, and disinformation, which usually contain numerical, categorical, textual, image, etc., data and used to initiate terrible outcomes (Ma et al 2016;Bharti et al 2017;Helmstetter and Paulheim 2018). Due to the high dependency on social media, many dishonest people get a chance to spread misinformation via a false account (Kumar and Shah 2018;Shu et al 2019c).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

195

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Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

195

View full text Add to dashboard Cite

show abstract

“…Finally, trained those classifiers using an extracted feature set to identify sarcasm in Twitter data. This work achieves an extensive precision improvement over existing systems [2].During the last decade majority of research has been carried out in the area of sentiment Analysis of textual data available on the web. Sentiment Analysis has its challenges, and one among them is Sarcasm.…”

Section: Sarcastic Detection Of Twitter Comments Using Pythonmentioning

confidence: 97%

Sarcastic Detection of Twitter Comments using Python

A.H*,

Wise.D.C,

et al. 2020

IJITEE

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A sarcasm / joke is a language of expressing the feeling in an opposite manner. In case of aural or cinematographic information the sarcasm can be identified easily through the tonal stress, gestures and facial expressions. But the main challenge of sarcasm is sarcastic detection in textual information where there is an absence of expressions or tonal stress. Presently a days, the utilization of informal organizations are enormously expanded. Most of the people express their feelings in their posts and comments in sarcastic manner. Sarcasm creates inquisitiveness and courtesy towards it in sentimental analysis. Wistful Analysis is the procedure or investigation of examining the sentiments. In this project, I have chosen twitter comments for this sarcastic sentimental analysis which is commonly an opinion mining. The importance of the project is to increase the accuracy rate by feeding huge data set for training. The purpose of finding the sarcasm in social networks is to block the user who points particularly or attack any victim which is not considered as sarcasm.

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“…They build a system for sarcasm detection using SVM classifier and used these indirect contradiction feature for training. Bharti et al [20] developed a machine learning framework to detect sarcastic tweets. They deployed several classical classifiers such as SVM, DT, NB, Maximum Entropy (ME), etc.…”

Section: Machine Learning-based Approachmentioning

confidence: 99%

Sarcasm as a Contradiction Between a Tweet and its Temporal Facts : A Pattern Based Approach

Bharti¹,

Babu²

2018

IJNLC

Self Cite

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In the context of Indian languages, sarcasm detection in Hindi is a tedious job as it is rich in morphology and complex in structure. The annotated resources for sarcastic Hindi sentences are almost negligible for machine learning analysis. Here, we propose a pattern-based framework for sarcasm detection in Hindi tweets. It has been observed that a tweet is sarcastic if it contradicts its temporal facts intentionally. The temporal fact is a collection of time-dependent facts which may change over the period. We used Hindi news with timestamp as a corpus of temporal facts. The timestamp describes the fact period of any entity. In this research, a temporal fact is represented as a pair. To form a pair, one need to extract triplets i.e. subject, verb and object for every sentence. Next, a key is formed using subject and verb together. The value is formed using object and timestamp together. To predict the sarcastic tweet; one needs to extract the triplets from input tweet and form a pair. Now, the pair of the input tweet is mapped with related pair in the corpus of temporal facts and are checked if they coincide. If they contradict, the input tweet is considered as sarcastic. The achieved accuracy of the proposed approach outperforms the state-of-the-arts techniques for Hindi sarcasm detection as it attains an accuracy of 82.8%.

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Sarcasm Analysis on Twitter Data Using Machine Learning Approaches

Cited by 25 publications

References 17 publications

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Sarcastic Detection of Twitter Comments using Python

Sarcasm as a Contradiction Between a Tweet and its Temporal Facts : A Pattern Based Approach

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