Sarcasm Detection in Sentiment Analysis Using Recurrent Neural Networks

In recent years, online opinionated textual data volume has surged, necessitating automated analysis to extract valuable insights. Data mining and sentiment analysis have become essential for analysing this type of text. Sentiment analysis is a text classification problem associated with many challenges, including better data preprocessing and sarcasm detection. Sarcasm in the text can reduce sentiment accuracy unless the model is specifically designed to identify such nuances. Sarcastic data conflicts with context, which leads to ambiguity. This article examines the impact of various preprocessing methods on sentiment classification. It presents a sarcasm detection–based sentiment analysis model that utilises an effective preprocessing system and a robust technique for identifying sarcastic text. The preprocessing method includes the impact of removing stop words, whitespaces, usernames, hashtags, and unnecessary URLs, mapping contractions dictionaries, designing word reference sheets, and stemming and lemmatization on the model. Sarcastic headlines are collected, and a word cloud is created using the frequency of words with sarcastic headlines. Custom sarcasm detection models based on long short‐term memory (LSTM), recurrent neural network (RNN), and word embeddings are utilised for sarcasm detection. The model has achieved a validation accuracy of 0.88 and an F ‐measure score of 72% for the overall system.