Sarcasm Detection over Social Media Platforms Using Hybrid Ensemble Model with Fuzzy Logic

Abstract

A figurative language expression known as sarcasm implies the complete contrast of what is being stated with what is meant, with the latter usually being rather or extremely offensive, meant to offend or humiliate someone. In routine conversations on social media websites, sarcasm is frequently utilized. Sentiment analysis procedures are prone to errors because sarcasm can change a statement’s meaning. Analytic accuracy apprehension has increased as automatic social networking analysis tools have grown. According to preliminary studies, the accuracy of computerized sentiment analysis has been dramatically decreased by sarcastic remarks alone. Sarcastic expressions also affect automatic false news identification and cause false positives. Because sarcastic comments are inherently ambiguous, identifying sarcasm may be difficult. Different individual NLP strategies have been proposed in the past. However, each methodology has text contexts and vicinity restrictions. The methods are unable to manage various kinds of content. This study suggests a unique ensemble approach based on text embedding that includes fuzzy evolutionary logic at the top layer. This approach involves applying fuzzy logic to ensemble embeddings from the Word2Vec, GloVe, and BERT models before making the final classification. The three models’ weights assigned to the probability are used to categorize objects using the fuzzy layer. The suggested model was validated on the following social media datasets: the Headlines dataset, the “Self-Annotated Reddit Corpus” (SARC), and the Twitter app dataset. Accuracies of 90.81%, 85.38%, and 86.80%, respectively, were achieved. The accuracy metrics were more accurate than those of earlier state-of-the-art models.

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

“…This design is specifically aimed at tackling the vanishing gradient problem, a common challenge in traditional RNNs that hampers their ability to recall information over extended periods, so LSTMs achieve this through the integration of memory cells and three distinct gates: input, output, and forget gates. These parts work together to control the flow of information, which lets the network store data for any amount of time and makes LSTMs perfect for tasks that require sequences of different lengths and long gaps between important events [37]. Consequently, LSTMs have become a cornerstone in deep learning (DL) for effectively categorizing and processing sequential and time-series data, overcoming the limitations associated with discharging and disappearing gradients that plagued earlier RNN models [29].…”

“…However, the authors used only a specific dataset that may not generalize across different languages or dialects, potential issues with the robustness of spam detection and sarcasm recognition exist across varied contexts, and the challenges of interpreting complex language nuances like irony or local slang could mean that the models' performance could be affected by dynamic changes in social media communication patterns, requiring ongoing adaptation and re-evaluation. Sharma et al [37] introduced a hybrid ensemble model for sarcasm detection, integrating fuzzy logic with Word2Vec, GloVe, and BERT embeddings. It reports high accuracies across different datasets, demonstrating the model's efficacy in understanding the nuanced language of sarcasm on social media.…”

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

“…Fuzzy logic outperforms Boolean logic in terms of the idea of truth. Fuzzy logic replaces the level of truth for Boolean truths consisting only of values 1 and 0 [85]. Fuzzy logic accepts the region between black and white (gray) as well as ambiguous linguistic terms such as "few", "passable", and "many" [86].…”

“…One of the most important components in the fuzzification step is the membership function. The mapping between 0 and 1 for each data point in the input space is determined by a membership function in the form of a fuzzy curve [85]. Membership in fuzzy sets has different shapes consisting of linear, bell, gaussian, trapezoidal, shoulder curves (left and right), and triangular shapes [103], [104].…”

Sarcasm detection in online comments using machine learning

“…Recently, Sharma et al (2023) incorporated word and phrase embeddings, including BERT, and used fuzzy logic evolutionary techniques to refine classification accuracy. This novel approach aimed to overcome the limitations of traditional deterministic models by introducing fuzzy reasoning, enabling improved handling of uncertainty and ambiguity in sarcasm detection.…”