188 research outputs found
"Did you really mean what you said?" : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word Embeddings
With the increased use of social media platforms by people across the world,
many new interesting NLP problems have come into existence. One such being the
detection of sarcasm in the social media texts. We present a corpus of tweets
for training custom word embeddings and a Hinglish dataset labelled for sarcasm
detection. We propose a deep learning based approach to address the issue of
sarcasm detection in Hindi-English code mixed tweets using bilingual word
embeddings derived from FastText and Word2Vec approaches. We experimented with
various deep learning models, including CNNs, LSTMs, Bi-directional LSTMs (with
and without attention). We were able to outperform all state-of-the-art
performances with our deep learning models, with attention based Bi-directional
LSTMs giving the best performance exhibiting an accuracy of 78.49%
QutNocturnal@HASOC'19: CNN for Hate Speech and Offensive Content Identification in Hindi Language
We describe our top-team solution to Task 1 for Hindi in the HASOC contest
organised by FIRE 2019. The task is to identify hate speech and offensive
language in Hindi. More specifically, it is a binary classification problem
where a system is required to classify tweets into two classes: (a) \emph{Hate
and Offensive (HOF)} and (b) \emph{Not Hate or Offensive (NOT)}. In contrast to
the popular idea of pretraining word vectors (a.k.a. word embedding) with a
large corpus from a general domain such as Wikipedia, we used a relatively
small collection of relevant tweets (i.e. random and sarcasm tweets in Hindi
and Hinglish) for pretraining. We trained a Convolutional Neural Network (CNN)
on top of the pretrained word vectors. This approach allowed us to be ranked
first for this task out of all teams. Our approach could easily be adapted to
other applications where the goal is to predict class of a text when the
provided context is limited
Challenges of Sarcasm Detection for Social Network : A Literature Review
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
Researchers eye-view of sarcasm detection in social media textual content
The enormous use of sarcastic text in all forms of communication in social
media will have a physiological effect on target users. Each user has a
different approach to misusing and recognising sarcasm. Sarcasm detection is
difficult even for users, and this will depend on many things such as
perspective, context, special symbols. So, that will be a challenging task for
machines to differentiate sarcastic sentences from non-sarcastic sentences.
There are no exact rules based on which model will accurately detect sarcasm
from many text corpus in the current situation. So, one needs to focus on
optimistic and forthcoming approaches in the sarcasm detection domain. This
paper discusses various sarcasm detection techniques and concludes with some
approaches, related datasets with optimal features, and the researcher's
challenges.Comment: 8 page
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