A Pattern-Based Approach for Sarcasm Detection on Twitter
Mondher Bouazizi,Tomoaki Ohtsuki +1 more
TLDR
This paper proposes a pattern-based approach to detect sarcasm on Twitter and proposes four sets of features that cover the different types of sarcasm, which are used to classify tweets as sarcastic and non-sarcastic.Abstract:
Sarcasm is a sophisticated form of irony widely used in social networks and microblogging websites. It is usually used to convey implicit information within the message a person transmits. Sarcasm might be used for different purposes, such as criticism or mockery. However, it is hard even for humans to recognize. Therefore, recognizing sarcastic statements can be very useful to improve automatic sentiment analysis of data collected from microblogging websites or social networks. Sentiment Analysis refers to the identification and aggregation of attitudes and opinions expressed by Internet users toward a specific topic. In this paper, we propose a pattern-based approach to detect sarcasm on Twitter. We propose four sets of features that cover the different types of sarcasm we defined. We use those to classify tweets as sarcastic and non-sarcastic. Our proposed approach reaches an accuracy of 83.1% with a precision equal to 91.1%. We also study the importance of each of the proposed sets of features and evaluate its added value to the classification. In particular, we emphasize the importance of pattern-based features for the detection of sarcastic statements.read more
Citations
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Journal ArticleDOI
Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection
TL;DR: This paper proposes an approach to detect hate expressions on Twitter based on unigrams and patterns that are automatically collected from the training set and used, among others, as features to train a machine learning algorithm.
Journal ArticleDOI
A Term Weighted Neural Language Model and Stacked Bidirectional LSTM Based Framework for Sarcasm Identification
Aytuğ Onan,Mansur Alp Tocoglu +1 more
TL;DR: In this paper, an effective sarcasm identification framework on social media data by pursuing the paradigms of neural language models and deep neural networks is presented. But sarcasm detection on text documents is one of the most challenging tasks in NLP.
Journal ArticleDOI
Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network
Le Hoang Son,Akshi Kumar,Saurabh Raj Sangwan,Anshika Arora,Anand Nayyar,Mohamed Abdel-Basset +5 more
TL;DR: A deep learning model called sAtt-BLSTM convNet that is based on the hybrid of soft attention-based bidirectional long short-term memory (sAtt- BLSTM) and convolution neural network (convNet) applying global vectors for word representation (GLoVe) for building semantic word embeddings is proposed.
Journal ArticleDOI
Systematic literature review of sentiment analysis on Twitter using soft computing techniques
Akshi Kumar,Arunima Jaiswal +1 more
TL;DR: This work presents a systematic literature review to collate, explore, understand, understand and analyze the efforts and trends in a well‐structured manner to identify research gaps defining the future prospects of this coupling of soft computing techniques for sentiment analysis on Twitter.
Journal ArticleDOI
Approaches to Cross-Domain Sentiment Analysis: A Systematic Literature Review
TL;DR: A comprehensive systematic literature review of the methods and techniques employed in a cross-domain sentiment analysis during the period of 2010–2016 to assist researchers in developing new and more accurate techniques in the future.
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