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The majority of the time, Sentiment Analysis is utilised to figure out what the author is thinking about. Major provocation has been occurring, and irony detection is believed to be one of the most provocative aspects of the situation currently. Irony is a unique manner of describing information that is in opposition to the topic being discussed, resulting in ambiguity. Data preparation, which covers numerous techniques such as lemmatization, tokenization, and stemming, is a main activity performed by the majority of software engineers. Many studies are carried out in the field of irony detection, which covers a variety of feature extraction algorithms. Support Vector Machine (SVM), linear regression, Nave Bayes, Random Forest, and other machine learning classifierswere employed in these studies, among others. The accuracy, precision, recall, and F-score obtained as a result of these research efforts may be utilized to forecast the most appropriate model for a given situation. In this study, we will explore the numerous methodologies that are employed in ironic text identification for Sentiment Analysis. [ABSTRACT FROM AUTHOR]

Sentiment Analysis is very important for many applications and tasks. It became very useful tool in market trends predictions, social media monitoring, sufficient predictor for election and products. Many approaches, by many researchers, were used for this task starting from rules-based to machine learning and deep learning approaches on different types of data. All of these methods suffer from a lack of accuracy as a result of not achieving the correct understanding of the texts. In this work, an approach for extracting and using the exact meaning of the word within the context is used as part of initialization data representation. Three well-known classifiers were used (Narve Bayes, Support Vector Machine, and Maximum entropy) for testing this approach on two languages (Arabic and English). The system was evaluated using precision, recall, f-measure, and accuracy on two different datasets (Stanford Sentiment Treebank for English language and LABR-V2 for Arabic language). The results showed that the exact meaning of the words is very useful and the accuracy was increased by range 7.3% for best cases and increased by range of 4% in worst cases. The contributions of this work can be summarized by: (i) using exact meaning of the word for two languages, (ii) using new approach for adding synonyms and antonyms for exact meaning of the word in context, and (iii) better accuracy was received compared with the baseline classifiers. [ABSTRACT FROM AUTHOR]