Using LSTM for Context Based Approach of Sarcasm Detection in Twitter

Khotijah, Siti; Tirtawangsa, Jimmy; Suryani, Arie Ardiyanti

doi:10.1145/3406601.3406624

Cited by 15 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Khotijah et al [40] (2020) applied Paragraph2vec to find the context in the tweets and then classified if the tweets were sarcastic or not using LSTM. Their method worked well for Indonesian tweets, but was found deficient owing to sparse dataset in case of English tweet.…”

Section: Figure 3 Data Collectionmentioning

confidence: 99%

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Shetty

Muniyal

Teja

et al. 2020

Preprint

View full text Add to dashboard Cite

Twitter is one of the most popular social networking platforms of today’s generation and is a fundamental tool in harvesting the data of many users worldwide. Many discussions ranging from current affairs, news sharing, filing complaints to advertising and discussing some common interests etc. happen on Twitter. It is widely used by many famous politicians as a prominent communication medium to address large masses ,owing to its mass usage and popularity . Thus, it is safe to assume that people communicate their political ideologies on Twitter. Many works have been done so far to deduce the stance of user towards a particular party by performing sentiment analysis on their tweets using popular classifiers. To find connected and similar users, earlier works generated a social network graph, based on the assumption that friends and followers share similar interests, which might not be true in all cases. In contrast, the proposed work employs the concept of ensemble classifier (a single classifier generated from several base learning classifiers) to analyze the tweets and makes use of multiple interaction elements like followers/ following, mentions, re-tweets ,hash tags etc. to infer which political party a user identifies with. These interaction elements project out the homophily (users who share same beliefs and choices) amongst the users. The proposed study can be encompassed to any domain and can be used by advertising agencies, marketing companies, e-commerce, heath care etc. to identify their target audience.

show abstract

Section: Figure 3 Data Collectionmentioning

confidence: 99%

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Shetty

Muniyal

Teja

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The selection of n i and n k is not a trivial task since these two values determine the size and number of data samples available for the model to be trained with (refer to section Data Reconditioning: Augmentation through Windowing), which naturally has a direct effect on model performance and uncertainty. Considering the differing lengths of the time-series data sets for each simulation case, acknowledging that they have been truncated for each mixing system (τ f = 385 and τ f = 98 for the stirred and static mixer, respectively), the values for n i and n k were fixed to (50,50) and (40,30) for the stirred and static mixer, respectively. These values were subjected to an early sensitivity test, but a full-scale tuning process would be required to discover the optimal configuration for each mixing case study.…”

Section: ■ Methodologymentioning

confidence: 99%

“…Previous works have implemented techniques to circumvent this flaw, such as padding, truncation, or “attention” mechanisms. The former two are common approaches implemented in text recognition and image processing, where the length of the longest (padding) or shortest (truncation) sequence is set as the standard, and each sequence is either filled with zeros or has data removed accordingly . Despite the benefits of longer padded sequences (Khotijah et al demonstrated consistently higher model accuracy when handling longer sequences) or the easiness of dealing with truncating data sets, other studies have suggested alternatives (e.g., nearest neighbor interpolation) arguing that padding can be computationally demanding and naive truncation methods can lead to critical information loss .…”

Section: Methodsmentioning

confidence: 99%

Liquid–Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks

Liang,

Valdes,

Cheng

et al. 2024

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

We demonstrate the application of a recurrent neural network (RNN) to perform multistep and multivariate timeseries performance predictions for stirred and static mixers as exemplars of complex multiphase systems. We employ two network architectures in this study, fitted with either long short-term memory and gated recurrent unit cells, which are trained on highfidelity, three-dimensional, computational fluid dynamics simulations of the mixer performance, in the presence and absence of surfactants, in terms of drop size distributions and interfacial areas as a function of system parameters; these include physicochemical properties, mixer geometry, and operating conditions. Our results demonstrate that while it is possible to train RNNs with a single fully connected layer more efficiently than with an encoder−decoder structure, the latter is shown to be more capable of learning long-term dynamics underlying dispersion metrics. Details of the methodology are presented, which include data preprocessing, RNN model exploration, and methods for model performance visualization; an ensemble-based procedure is also introduced to provide a measure of the model uncertainty. The workflow is designed to be generic and can be deployed to make predictions in other industrial applications with similar time-series data.

show abstract

“…Karthik Sundararajan and others proposed a sarcasm detecting approach and irony type using Multi-Rule Based Ensemble Feature Selection Model [17]. Siti Khotija, Khadijah Tirtawangsa, Arie A Suryani suggested a contextbased method to detect sarcasm in tweets based on Long Short-Term Memory (LSTM) [18]. According to another paper, classifier's performance is vital in sarcasm predictions in opinion mining [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Sarcasm Detection in Tweets: A Feature-based Approach using Supervised Machine Learning Models

Rahaman¹,

Kuri²,

Islam³

et al. 2021

IJACSA

View full text Add to dashboard Cite

Sarcasm (i.e., the use of irony to mock or convey contempt) detection in tweets and other social media platforms is one of the problems facing the regulation and moderation of social media content. Sarcasm is difficult to detect, even for humans, due to the deliberate ambiguity in using words. Existing approaches to automatic sarcasm detection primarily rely on lexical and linguistic cues. However, these approaches have produced little or no significant improvement in terms of the accuracy of sentiment. We propose implementing a robust and efficient system to detect sarcasm to improve accuracy for sentiment analysis. In this study, four sets of features include various types of sarcasm commonly used in social media. These feature sets are used to classify tweets into sarcastic and nonsarcastic. This study reveals a sarcastic feature set with an effective supervised machine learning model, leading to better accuracy. Results show that Decision Tree (91.84%) and Random Forest (91.90%) outperform in terms of accuracy compared to other supervised machine learning algorithms for the right features selection. The paper has highlighted the suitable supervised machine learning models along with its appropriate feature set for detecting sarcasm in tweets.

show abstract

Using LSTM for Context Based Approach of Sarcasm Detection in Twitter

Cited by 15 publications

References 10 publications

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Liquid–Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks

Sarcasm Detection in Tweets: A Feature-based Approach using Supervised Machine Learning Models

Contact Info

Product

Resources

About