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Sentiment analysis has observed a nascent interest over the past decade in the field of social media analytics. With major advances in the volume, rationality and veracity of social networking data, the misunderstanding, uncertainty and inaccuracy within the data have multiplied. In the textual data, the location of sarcasm is a challenging task. It is a different way of expressing sentiments, in which people write or says something different than what they actually intended to. So, the researchers are showing interest to develop various techniques for the detection of sarcasm in the texts to boost the performance of sentiment analysis. This paper aims to overview the sentiment analysis, sarcasm and related work for sarcasm detection. Further, this paper provides training to health-care professionals to make the decision on the patient’s sentiments.

This paper has compared the performance of five different classifiers – support vector machine, naïve Bayes classifier, decision tree classifier, AdaBoost classifier and K-nearest neighbour on the Twitter data set.

This paper has observed that naïve Bayes has performed the best having the highest accuracy of 61.18%, and decision tree performed the worst with an accuracy of 54.27%. Accuracy of AdaBoost, K-nearest neighbour and support vector machine measured were 56.13%, 54.81% and 59.55%, respectively.

This research work is original.

Sarcasm is a sentiment in which human beings convey messages with the opposite meanings to hurt someone emotionally or condemn something in a witty manner. The difference between the text's literal and its intended meaning makes it tough to identify. Mostly, researchers and practitioners only consider explicit information for text classification; however, considering implicit with explicit information will enhance the classifier's accuracy. Several sarcasm detection studies focus on syntactic, lexical or pragmatic features that are uttered using words, emoticons and exclamation marks. Discrete models, which are utilized by many existing works, require manual features that are costly to uncover.

In this research, word embeddings used for feature extraction are combined with context-aware language models to provide automatic feature engineering capabilities as well superior classification performance as compared to baseline models. Performance of the proposed models has been shown on three benchmark datasets over different evaluation metrics namely misclassification rate, receiver operating characteristic (ROC) curve and area under curve (AUC).

Experimental results suggest that FastText word embedding technique with BERT language model gives higher accuracy and helps to identify the sarcastic textual element correctly.

Sarcasm detection is a sub-task of sentiment analysis. To help in appropriate data-driven decision-making, the sentiment of the text that gets reversed due to sarcasm needs to be detected properly. In online social environments, it is critical for businesses and individuals to detect the correct sentiment polarity. This will aid in the right selling and buying of products and/or services, leading to higher sales and better market share for businesses, and meeting the quality requirements of customers.

Sarcasm is often used in everyday speech and writing and is prevalent in online contexts. The purpose of this paper is to investigate the analogy between sarcasm comments from sentiment tools and the human coder.

Using the Verbal Irony Procedure, eight human coders were engaged to analyse comments collected from an online commercial page, and a dissimilarity analysis was conducted with sentiment tools. Three constants were tested, namely, polarity from sentiment tools, polarity rating by human coders; and sarcasm-level ratings by human coders.

Results found an inconsistent ratio between these three constants. Sentiment tools used did not have the capability or reliability to detect the subtle, contextualized meanings of sarcasm statements that human coders could detect. Further research is required to refine the sentiment tools to enhance their sensitivity and capability.

With these findings, it is recommended that further research and commercialization efforts be directed at improving current sentiment tools – for example, to incorporate sophisticated human sarcasm texts in their analytical systems. Sarcasm exists frequently in media, politics and human forms of communications in society. Therefore, more highly sophisticated sentiment tools with the abilities to detect human sarcasm would be vital in research and industry.

The findings suggest that presently, of the sentiment tools investigated, most are still unable to pick up subtle contexts within the text which can reverse or change the message that the writer intends to send to his/her receiver. Hence, the use of the relevant hashtags (e.g. #sarcasm; #irony) are of fundamental importance in detection tools. This would aid the evaluation of product reviews online for commercial usage.

The value of this study lies in its original, empirical findings on the inconsistencies between sentiment tools and human coders in sarcasm detection. The current study proves these inconsistencies are detected between human and sentiment tools in social media texts and points to the inadequacies of current sentiment tools. With these findings, it is recommended that further research and commercialization efforts be directed at improving current sentiment tools – to incorporate sophisticated human sarcasm texts in their analytical systems. The system can then be used as a reference for psychologists, media analysts, researchers and speech writers to detect cues in the inconsistencies in behaviour and language.

The purpose of this paper is to study sarcasm in online text – specifically on twitter – to better understand customer opinions about social issues, products, services, etc. This can be immensely helpful in reducing incorrect classification of consumer sentiment toward issues, products and services.

In this study, 5,000 tweets were downloaded and analyzed. Relevant features were extracted and supervised learning algorithms were applied to identify the best differentiating features between a sarcastic and non-sarcastic sentence.

The results using two different classification algorithms, namely, Naïve Bayes and maximum entropy show that function words and content words together are most effective in identifying sarcasm in tweets. The most differentiating features between a sarcastic and a non-sarcastic tweet were identified.

Understanding the use of sarcasm in tweets let companies do better sentiment analysis and product recommendations for users. This could help businesses attract new customers and retain the old ones resulting in better customer management.

This paper uses novel features to identify sarcasm in online text which is one of the most challenging problems in natural language processing. To the authors’ knowledge, this is the first study on sarcasm detection from a customer management perspective.

Technology-enabled communication used in workplace settings includes nuanced tools such as emojis, that are interpreted differently by different populations of people. This paper aims to evaluate the use of emojis in work environments, particularly when they are used sarcastically.

This research uses a survey method administered on MTurk. Overall, 200 participants were included in the analysis. Items were contextualized from prior research and offered on a seven-point Likert scale.

Females are better able to understand if an emoji is used sarcastically. Additionally, older employees are more capable of interpreting sarcasm than younger employees. Finally, understanding of emojis has a negative relationship with frustration, indicating that when users understand emojis are being used sarcastically, frustration is reduced.

This research is primarily limited by the survey methodology. Despite this, it provides implications for theory of mind and practical understanding of emoji use in professional settings. This research indicates emojis are often misinterpreted in professional settings.

The use of emojis is becoming commonplace. The authors show the use of emojis in a professional setting creates confusion, and in some instances can lead to frustration. This work can help businesses understand how best to manage employees with changing communication tools.

With the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.

This study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.

Based on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.

At the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.

This study aimed to assess the potential of the Clinical Dementia Rating (CDR) Scale in the prognosis of dementia in elderly subjects.

Dementia staging severity is clinically an essential task, so the authors used machine learning (ML) on the magnetic resonance imaging (MRI) features to locate and study the impact of various MR readings onto the classification of demented and nondemented patients. The authors used cross-sectional MRI data in this study. The designed ML approach established the role of CDR in the prognosis of inflicted and normal patients. Moreover, the pattern analysis indicated CDR as a strong cohort amongst the various attributes, with CDR to have a significant value of p < 0.01. The authors employed 20 ML classifiers.

The mean prediction accuracy varied with the various ML classifier used, with the bagging classifier (random forest as a base estimator) achieving the highest (93.67%). A series of ML analyses demonstrated that the model including the CDR score had better prediction accuracy and other related performance metrics.

The results suggest that the CDR score, a simple clinical measure, can be used in real community settings. It can be used to predict dementia progression with ML modeling.

The proliferation of digital communication on social media provides new opportunities for businesses to take advantage of Internet memes to boost customer engagement. Academic literature on digital communications mostly focuses on popular forms such as selfies, branded posts, and branded emoticons. Less attention has been paid to brand memes and their implications for brand management. Based on the cue utilization theory, this research aims to investigate the informational cues of brand memes foster brand partnerships.

The structural equation modeling and importance-performance matrix analysis were used to empirically validate the research hypotheses with 595 respondents to an online survey.

Three informational cues of brand memes (i.e. comprehensibility, novelty, and meme-brand congruity) stimulated consumers' attitudes, which in turn impacted consumer-brand relationships. Another brand meme informational cue, sarcasm, negatively moderated the relationships between the three informational cues and consumer-brand relationships.

Our findings indicate that a brand can engage consumers in conversations on social media and foster long-term consumer-brand relationships through brand memes.

Purpose: Previous research identified a measurement gap in the individual assessment of social misconduct in the workplace related to gender. This gap implies that women respond to comparable self-reported acts of bullying or sexual discrimination slightly more often than men with the self-labeling as “bullied” or “sexually discriminated and/or harassed.” This study tests this hypothesis for women and men in the scientific workplace and explores patterns of gender-related differences in self-reporting behavior.

Basic design: The hypotheses on the connection between gender and the threshold for self-labeling as having been bullied or sexually discriminated against were tested based on a sample from a large German research organization. The sample includes 5,831 responses on bullying and 6,987 on sexual discrimination (coverage of 24.5 resp. 29.4 percentage of all employees). Due to a large number of cases and the associated high statistical power, this sample for the first time allows a detailed analysis of the “gender-related measurement gap.” The research questions formulated in this study were addressed using two hierarchical regression models to predict the mean values of persons who self-labeled as having been bullied or sexually discriminated against. The status of the respondents as scientific or non-scientific employees was included as a control variable.

Results: According to a self-labeling approach, women reported both bullying and sexual discrimination more frequently. This difference between women and men disappeared for sexual discrimination when, in addition to the gender of a person, self-reported behavioral items were considered in the prediction of self-labeling. For bullying, the difference between the two genders remained even in this extended prediction. No statistically significant relationship was found between the frequency of self-reported items and the effect size of their interaction with gender for either bullying or sexual discrimination. When comparing bullying and sexual discrimination, it should be emphasized that, on average, women report experiencing a larger number of different behavioral items than men.

Interpretation and relevance: The results of the study support the current state of research. However, they also show how volatile the measurement instruments for bullying and sexual discrimination are. For example, the gender-related measurement gap is considerably influenced by single items in the Negative Acts Questionnaire and Sexual Experience Questionnaire. The results suggest that women are generally more likely than men to report having experienced bullying and sexual discrimination. While an unexplained “gender gap” in the understanding of bullying was found for bullying, this was not the case for sexual discrimination.

This article presents two Brazilian Portuguese corpora collected from different media concerning public security issues in a specific location. The primary motivation is supporting analyses, so security authorities can make appropriate decisions about their actions.

The corpora were obtained through web scraping from a newspaper's website and tweets from a Brazilian metropolitan region. Natural language processing was applied considering: text cleaning, lemmatization, summarization, part-of-speech and dependencies parsing, named entities recognition, and topic modeling.

Several results were obtained based on the methodology used, highlighting some: an example of a summarization using an automated process; dependency parsing; the most common topics in each corpus; the forty named entities and the most common slogans were extracted, highlighting those linked to public security.

Some critical tasks were identified for the research perspective, related to the applied methodology: the treatment of noise from obtaining news on their source websites, passing through textual elements quite present in social network posts such as abbreviations, emojis/emoticons, and even writing errors; the treatment of subjectivity, to eliminate noise from irony and sarcasm; the search for authentic news of issues within the target domain. All these tasks aim to improve the process to enable interested authorities to perform accurate analyses.

The corpora dedicated to the public security domain enable several analyses, such as mining public opinion on security actions in a given location; understanding criminals' behaviors reported in the news or even on social networks and drawing their attitudes timeline; detecting movements that may cause damage to public property and people welfare through texts from social networks; extracting the history and repercussions of police actions, crossing news with records on social networks; among many other possibilities.

The work on behalf of the corpora reported in this text represents one of the first initiatives to create textual bases in Portuguese, dedicated to Brazil's specific public security domain.