Proposed Approach for Sarcasm Detection in Twitter

Saha, Shubhodip; Yadav, Jainath; Ranjan, Prabhat

doi:10.17485/ijst/2017/v10i25/114443

Cited by 54 publications

(32 citation statements)

References 1 publication

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“…Authors of [53] used Textblob for pre-processing, polarity, the polarity confidence calculation, and they validated the obtained results by SVM and Naïve Bayes using Weka; they reported the highest accuracy of Naïve Bayes with a 65.2% rate, which was 5.1% more than the SVM accuracy rate. An unsupervised machine-learning algorithm was introduced [54] to rate the reviews as thumbs up and thumbs down; 410 reviews were gathered from Epinions and tested, the observed accuracy of the algorithm was 74%.…”

Section: Background Study/literature Surveymentioning

confidence: 90%

Machine Learning-Based Sentiment Analysis for Twitter Accounts

Hasan

Moin

Karim

et al. 2018

MCA

346

158

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Growth in the area of opinion mining and sentiment analysis has been rapid and aims to explore the opinions or text present on different platforms of social media through machine-learning techniques with sentiment, subjectivity analysis or polarity calculations. Despite the use of various machine-learning techniques and tools for sentiment analysis during elections, there is a dire need for a state-of-the-art approach. To deal with these challenges, the contribution of this paper includes the adoption of a hybrid approach that involves a sentiment analyzer that includes machine learning. Moreover, this paper also provides a comparison of techniques of sentiment analysis in the analysis of political views by applying supervised machine-learning algorithms such as Naïve Bayes and support vector machines (SVM).

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Section: Background Study/literature Surveymentioning

confidence: 90%

Machine Learning-Based Sentiment Analysis for Twitter Accounts

Hasan

Moin

Karim

et al. 2018

MCA

346

158

View full text Add to dashboard Cite

show abstract

“…This work conducted experiment on online product reviews and measured the performance in terms of overall accuracy, average precision, average recall and average F-Measure. Shubhodip Saha et al [25] detected sarcasm by analysing the Twitter posts using Naïve Bayes and SVM classifiers and its accuracy was calculated using WEKA tool. For pre processing of data, TextBlob was used, which is one of the Natural Language Toolkit (NLT) packages.…”

Section: A Sentiment Analysismentioning

confidence: 99%

“…This research focuses on finding sentiments from Twitter data. Most of the researchers dealt with various ML approaches of sentiment analysis [20][21][22][23][24][25], and just classified sentiments as positive and negative sentiments. Hence, this research work addresses the likes, dislikes and character of the persons in addition to regular sentiment analysis, which will be helpful to know the complete personality of a person.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-based Online Social Network Analysis for 360-degree User Profiling

2019

IJITEE

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This paper aims to analyse the online social network for reconnaissance of people for finding their potentiality. The work considers one of the famous social networking sites, Twitter, where people express their thoughts and ideas, through which the people in the site knowingly or unknowingly reveal the information about themselves such as personal interests, likes and dislikes. The Machine Learning technique facilitates the work to mine the tweet data of a person to get his/her 360-degree profiling. This profiling is helpful to identify the personality type of a person, which is essential for the Government to identify the people involved in spreading the fake news in Twitter.

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“…With the help of sarcasm detection techniques, companies can analyze customers’ feelings about their products. This provides crucial help for those companies to boost their product quality (Saha et al, 2017). In sentiment analysis, the sarcasm classification is an essential subtask (Cambria et al, 2015), especially in classifying tweets, for conveying implicit information within the message that a person expresses or shares with others.…”

Section: Introductionmentioning

confidence: 99%

Sarcasm detection using machine learning algorithms in Twitter: A systematic review

Sarsam

Al-Samarraie

Alzahrani

et al. 2020

International Journal of Market Research

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Recognizing both literal and figurative meanings is crucial to understanding users’ opinions on various topics or events in social media. Detecting the sarcastic posts on social media has received much attention recently, particularly because sarcastic comments in the form of tweets often include positive words that represent negative or undesirable characteristics. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to understand the application of different machine learning algorithms for sarcasm detection in Twitter. Extensive database searching led to the inclusion of 31 studies classified into two groups: Adapted Machine Learning Algorithms (AMLA) and Customized Machine Learning Algorithms (CMLA). The review results revealed that Support Vector Machine (SVM) was the best and the most commonly used AMLA for sarcasm detection in Twitter. In addition, combining Convolutional Neural Network (CNN) and SVM was found to offer a high prediction accuracy. Moreover, our result showed that using lexical, pragmatic, frequency, and part-of-speech tagging can contribute to the performance of SVM, whereas both lexical and personal features can enhance the performance of CNN-SVM. This work also addressed the main challenges faced by prior scholars when predicting sarcastic tweets. Such knowledge can be useful for future researchers or machine learning developers to consider the major issues of classifying sarcastic posts in social media.

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Proposed Approach for Sarcasm Detection in Twitter

Cited by 54 publications

References 1 publication

Machine Learning-Based Sentiment Analysis for Twitter Accounts

Machine Learning-Based Sentiment Analysis for Twitter Accounts

A Machine Learning-based Online Social Network Analysis for 360-degree User Profiling

Sarcasm detection using machine learning algorithms in Twitter: A systematic review

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