Sarcastic Sentences Research Articles

Impact Of Sarcasm Detection on Sentiment Analysis Using Bi-LSTM and FastText

Sentiment analysis categorizes a collection of texts in a document as either positive or negative. However, sometimes it cannot give accurate results due to sarcastic sentences. Sarcasm involves the use of positive language to convey negative meanings, So sarcasm detection is needed for sentiment classification to provide better results. One method that can be used to perform Sentiment classification is Bidirectional Long Short-Term Memory (Bi-LSTM). However, text data cannot be processed by Bi-LSTM, so it requires word embedding to convert text data into vectors. In this study, the word embedding used is FastText because it can learn the form of words by considering subword information. The results showed that sentiment classification with sarcasm detection could improve evaluation results by 0.08 for precision, 0.07 for recall, 0.07 for F1-score, and 0.07 for accuracy. A paired sample t-test was conducted on precision, recall, F1-score, and accuracy to examine whether there is a difference between sentiment classification with and without sarcasm detection. The obtained p-values are 2.84.10-9, 4.63.10-7, and 2.40.10-8, 6.22.10-8, respectively. This indicates a difference between sentiment classification with and without sarcasm detection. Therefore, with a 95% confidence level, it can be concluded that sarcasm detection impacts sentiment classification.

Pelabelan Sentimen Berbasis Semi-Supervised Learning menggunakan Algoritma LSTM dan GRU

In the sentiment analysis research process, there are problems when still using manual labeling methods by humans (expert annotation), which are related to subjectivity, long time, and expensive costs. Another way is to use computer assistance (machine annotator). However, the use of machine annotators also has the research problem of not being able to detect sarcastic sentences. Thus, the researcher proposed a sentiment labeling method using Semi-Supervised Learning. Semi-supervised learning is a labeling method that combines human labeling techniques (expert annotation) and machine labeling (machine annotation). This research uses machine annotators in the form of Deep Learning algorithms, namely the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The word weighting method used in this research is Word2Vec Continuous Bag of Word (CBoW). The results showed that the GRU algorithm tends to have a better accuracy rate than the LSTM algorithm. The average accuracy of the training results of the LSTM and GRU algorithm models is 0.904 and 0.913. In contrast, the average accuracy of labeling by LSTM and GRU is 0.569 and 0.592, respectively.

Sarcasm Detection in News Headline Dataset with Ensemble Deep Learning Method

Sarcasm, a prevalent linguistic device, is frequently used in public discourse, often causing offence and distress to the listener. The complexity inherent in detecting sarcasm is a significant and ongoing challenge in the field of sentiment analysis research. The widespread use of this phenomenon in diverse conversational contexts further complicates its identification in data sets full of human interactions. Deficiencies in methodologies for distinguishing such statements adversely affect the performance of sentiment analysis, especially in distinguishing negative, positive or neutral sentiments. Inaccuracies in sarcasm detection can affect the classification results of sentiment analysis. Therefore, sentiment analysis seeks to categorise sarcastic sentences that, despite appearing positive, actually contain negative meanings. This research aims to build a deep learning ensemble stack model. The basic deep learning methods used are Bidirectional Gated Recurrent Unit (BiGRU) and Convolutional Neural Network (CNN). LightGBM is used to perform stack ensemble of deep learning methods. The dataset used comes from the Kaggle website and consists of English headlines. The findings show that the stack ensemble method outperforms BiGRU and CNN, evidenced by an accuracy rate of 91.2% and an F1 score of 90.2%. Therefore, from the above discussion, it can be concluded that the LightGBM method emerges as the optimal solution for sarcasm detection

Systematic review of sentiment analysis and predict sarcastic

With the rise of social media on the web, sentiment analysis has become one of the most important areas of study. Today, millions of people share their thoughts, ideas, feelings, and opinions on social media sites like Twitter and Facebook. Sentiment analysis, also called opinion mining, is mostly about classifying and predicting how people feel about a certain target. It involves putting text documents or sentences into groups based on how positive or negative they are about a certain topic. Researchers always find "natural language processing" to be one of the most interesting topics. To solve different problems and improve the accuracy of different applications, it is always helpful to know the exact meaning of what is being said in a conversation. Sentiment analysis uses natural language processing (NLP) and learning models like machine learning and deep learning algorithms to figure out how people feel about the data given. Sentiment analysis looks at sarcasm because sarcasm is a way for people to say how they feel about something without saying it directly. People means the exact opposite of what the sentence says at first glance. Sarcasm is hard to figure out because each sarcastic sentence is different. This paper will talk about what has been done in the field of sarcasm detection, the different techniques used, and the problems that still need to be solved.

Sentiment Analysis and Sarcasm Detection using Deep Multi-Task Learning.

Social media platforms such as Twitter and Facebook have become popular channels for people to record and express their feelings, opinions, and feedback in the last decades. With proper extraction techniques such as sentiment analysis, this information is useful in many aspects, including product marketing, behavior analysis, and pandemic management. Sentiment analysis is a technique to analyze people's thoughts, feelings and emotions, and to categorize them into positive, negative, or neutral. There are many ways for someone to express their feelings and emotions. These sentiments are sometimes accompanied by sarcasm, especially when conveying intense emotion. Sarcasm is defined as a positive sentence with underlying negative intention. Most of the current research work treats them as two distinct tasks. To date, most sentiment and sarcasm classification approaches have been treated primarily and standalone as a text categorization problem. In recent years, research work using deep learning algorithms have significantly improved performance for these standalone classifiers. One of the major issues faced by these approaches is that they could not correctly classify sarcastic sentences as negative. With this in mind, we claim that knowing how to spot sarcasm will help sentiment classification and vice versa. Our work has shown that these two tasks are correlated. This paper proposes a multi-task learning-based framework utilizing a deep neural network to model this correlation to improve sentiment analysis's overall performance. The proposed method outperforms the existing methods by a margin of 3%, with an F1-score of 94%.

New Modern Approach to Predict Users’ Sentiment Using CNN and BLSTM

In Today’s world social network play a vital role and provides relevant information on user opinion. This paper presents emotional health monitoring system to detect stress and the user mood. Depending on results the system will send happy, calm, relaxing or motivational messages to users with phycological disturbance. It also sends warning messages to authorized persons in case a depression disturbance is detected by monitoring system. This detection of sentence is performed through convolution neural network (CNN) and bi-directional long-term memory (BLSTM). This method reaches accuracy of 0.80 to detect depressed and stress users and also system consumes low memory, process and energy. We can do the future work of this project by also including the sarcastic sentences in the dataset. We can also predict the sarcastic data with the proposed algorithm

Detecting the target of sarcasm is hard: Really??

Sarcasm target detection (identifying the target of mockery in a sarcastic sentence) is an emerging field in computational linguistics. Although there has been some research in this field, accurately identifying the target still remains problematic especially when the target of mockery is not presented in the text. In this paper, we propose a combination of a machine learning classifier and a deep learning model to extract the target of sarcasm from the text. First, we classify sarcastic sentences using machine learning, to determine whether a sarcastic sentence contains a target. Then we use a deep learning model from Aspect-Based Sentiment Analysis to extract the target. Our proposed system is evaluated on three publicly available data sets: sarcastic book snippets, sarcastic tweets, and sarcastic Reddit comments. Our evaluation results show that our approach achieves equal or better performance compared to the current state-of-the-art system, with an 18% improvement on the Reddit data set and similar scores on the Books and Tweets data sets. This is because our method is able to accurately identify when the target of sarcasm is not present. The primary challenge we identify, that is hindering the creation of a high accuracy classifier, is the lack of consistency among human annotators in identifying the target of sarcasm within standard ground-truth data sets.

Sense in the nonsense: Deciphering the meaning of u kumela in Tshivenḓa culture

U kumela (paying homage to the royal leader) is a widely accepted practice in Tshivenḓa culture. U kumela not only serves as an aspect of praise to royal leadership but also functions as an addressive device. Zwikumelo (‘referential praises’) often have no definite or rigid pattern and are uncensored. As such, the praise singer may start the way he or she wishes and utter whatever he or she likes. Hence, praise singers often utter somewhat humorous, sarcastic, hyperbolic and senseless phrases and sentences when bestowing honour upon royalty. Undergirded by the theory of ethnopragmatics, this study employs the qualitative method (descriptive and explanatory in nature) with the aim of generating the communicative translations and meanings of zwikumelo in Tshivenḓa culture. Abetted by the author’s intuitive knowledge, this study interpreted zwikumelo in MER Mathivha’s Tshivenḓa play, Mabalanganye. It was found that most zwikumelo in the play ascribe qualities such as immortality, sovereignty, unapproachability, bravery and generosity to the chief or king.

AI-Based Learning Techniques for Sarcasm Detection of Social Media Tweets: State-of-the-Art Survey

Sarcasm, though difficult to define but plays a crucial role in one’s life. Sarcasm as a jest is a matter of fun but when taken seriously can cause unwelcoming results. Sometimes, sarcasm is defined as “a sharp, bitter, or cutting expression or remark; a bitter jibe or taunt”. These days’ researchers are working towards the detection of sarcasm for the purpose of sentiment analysis. Emotion and sentiment-bearing information are carried by subjective sarcastic sentences. The objective of the paper is to highlight the different types of sarcastic tweets and their usage in sentiment analysis. The authors mainly emphasize several approaches which include sentiment analysis, machine and deep learning classifications. The paper focuses on the use of machine learning and deep learning for identifying sarcastic tweets. Numerous feature extraction techniques have been studied and machine and deep learning classifications have been taken into account. The comparative table shows the results obtained using the various evaluation metrics such as accuracy, precision, recall, and f-score.

RETRACTED ARTICLE: Sentiment topic sarcasm mixture model to distinguish sarcasm prevalent topics based on the sentiment bearing words in the tweets

Sentiment analysis as we all know is a developed field in which new features keeps on adding, but most of the time, on the internet people use sarcasm to convey their message which is very difficult to understand both by people and machines. Sarcastic statements are very complex as most of the time they sound in positive context if interpreted literally but actually the speaker mean the opposite of what they speak. Sarcasm detection is a subtask of opinion mining. The main intention behind sarcasm detection is to identify the user opinions or emotions expressed by the user in the written text. It plays a critical role in sentiment analysis by correctly identifying sarcastic or non sarcastic sentences. The sarcastic sentence has mixed polarity of both positive and negative words. Understanding sarcasm is quite a difficult and a challenging task even for humans as well as for machines. Various approaches for sarcasm detection are purely based on machine learning classifiers where training the classifier is based on simple lexical or dictionary based features. The objective of the work is to develop an unsupervised probabilistic relational model to identify sarcasm prevalent topics based on the sentiment distribution of the words in the tweets. The model estimates sentiment based topic level distribution. The model evaluation shows the sentiment associated words that do appear in the short text given the sentiment related label. The model outperforms the other baseline state of art Model for sarcasm detection as shown in the experimental result and it is very much suited for the prediction of sarcasm of a short tweet.

Hyperbolic Feature-based Sarcasm Detection in Telugu Conversation Sentences

AbstractRecognition of sarcastic statements has been a challenge in the process of sentiment analysis. A sarcastic sentence contains only positive words conveying a negative sentiment. Therefore, it is tough for any automated machine to identify the exact sentiment of the text in the presence of sarcasm. The existing systems for sarcastic sentiment detection are limited to the text scripted in English. Nowadays, researchers have shown greater interest in low resourced languages such as Hindi, Telugu, Tamil, Arabic, Chinese, Dutch, Indonesian, etc. To analyse these low resource languages, the biggest challenge is the lack of available resources, especially in the context of Indian languages. Indian languages are very rich in morphology which pose a greater challenge for the automated machines. Telugu is one of the most popular languages after Hindi among Indian languages. In this article, we have collected and annotated a corpus of Telugu conversation sentences in the form of a question followed by a reply for sarcasm detection. Further, a set of algorithms are proposed for the analysis of sarcasm in the corpus of Telugu conversation sentences. The proposed algorithms are based on hyperbolic features namely, Interjection, Intensifier, Question mark and Exclamation symbol. The achieved accuracy is 94%.

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter.

Sentimental analysis aims at inferring how people express their opinion over any piece of text or topic of interest. This article deals with detection of an implicit form of the sentiment, referred to as sarcasm. Sarcasm conveys the opposite of what people try to convey in order to criticize or ridicule in a humorous way. It plays a vital role in social networks since most of the tweets or posts contain sarcastic nuances. Existing approaches towards the study of sarcasm deals only with the detection of sarcasm. In this paper, in addition to detecting sarcasm from text, an approach has been proposed to identify the type of sarcasm. The main motivation behind determining the types of sarcasm is to identify the level of hurt or the true intent behind the sarcastic text. The proposed work aims to improve upon the existing approaches by incorporating a new perspective which classifies the sarcasm based on the level of harshness employed. The major application of the proposed work would be relating the emotional state of a person to the type of sarcasm exhibited by him/her which could provide major insights about the emotional behavior of a person. An ensemble-based feature selection method has been proposed for identifying the optimal set of features needed to detect sarcasm from tweets. This optimal set of features was employed to detect whether the tweet is sarcastic or not. After detecting sarcastic sentences, a multi-rule based approach has been proposed to determine the type of sarcasm. As an initial attempt, sarcasm has been classified into four types, namely, polite sarcasm, rude sarcasm, raging sarcasm, and deadpan sarcasm. The performance and efficiency of the proposed approach has been experimentally analyzed, and change in mood of a person for each sarcastic type has been modelled. The overall accuracy of the proposed ensemble feature selection algorithm for sarcasm detection is around 92.7%, and the proposed multi-rule approach for sarcastic type identification achieves an accuracy of 95.98%, 96.20%, 99.79%, and 86.61% for polite, rude, raging, and deadpan types of sarcasm, respectively.

Tahun Politik, Komunikasi Konfrontatif

Term “Confrontative Communication” popular in Indonesia when the election of Governor ofJakarta at 2012. In that year, one candidate often talk intimidation people and his competitor withusing sarcastic sentence, for example “If you will not choose Betawi man, please out from Jakarta orI will disarm your facilities included your KTP (kartu tanda penduduk, identification card)”.Confrontative Communication different with Confrontation Communication in medical work which isdone by doctor or nurse. Confrontative Communication is a part of coersive communication which isused in political communication. Every candidate use confrontative communication to defeatcompetitor; among them use it each other.Now, in the politics year for winning the election especially president election, confrontativecommunication without controlled by politics ethics tends to be used in political communication. Thisarticle want to clarify that phenomena.Key words: political communication, coersive communication, confrontative communication.

Sentiment analysis: a review and comparative analysis over social media

Sentiment analysis is the computational examination of end user’s opinion, attitudes and emotions towards a particular topic or product. Sentiment analysis classifies the message according to their polarity whether it is positive, negative, or neutral. Recently researchers focused on lexical and machine-learning based method for sentiment analysis of social media post. Social media is a micro blogger site in which end users can post their comment in slag language that contains symbols, idioms, misspelled words and sarcastic sentences. Social media data also have curse of dimension problem i.e. high dimension nature of data that required specific pre-processing and feature extraction, which leads to improve classification accuracy. This paper present comprehensive overview of sentiment analysis technique based on recent research and subsequently explores machine learning (SVM, Navies Bayes, Linear Regression and Random Forest) and feature extraction techniques (POS, BOW and HASS tagging) in context of Sentiment analysis over social media data set. Further twitter data-sets are scrutinized and pre-processed with proposed framework,which yield intersecting facts about the capabilities and deficiency of sentiment analysis methods. POS is most suitable feature extraction technique with SVM and Navie Bayes classifier. Whereas Random Forest and linear regression provide the better result with Hass tagging.

ICWSM — A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews

Sarcasm is a sophisticated form of speech act widely used in online communities. Automatic recognition of sarcasm is, however, a novel task. Sarcasm recognition could contribute to the performance of review summarization and ranking systems. This paper presents SASI, a novel Semi-supervised Algorithm for Sarcasm Identification that recognizes sarcastic sentences in product reviews. SASI has two stages: semi-supervised pattern acquisition, and sarcasm classification. We experimented on a data set of about 66000 Amazon reviews for various books and products. Using a gold standard in which each sentence was tagged by 3 annotators, we obtained precision of 77% and recall of 83.1% for identifying sarcastic sentences. We found some strong features that characterize sarcastic utterances. However, a combination of more subtle pattern-based features proved more promising in identifying the various facets of sarcasm. We also speculate on the motivation for using sarcasm in online communities and social networks.