Sentiment Recognition of Hinglish Code Mixed Data using Deep Learning Models based Approach

Sentiment analysis in social media content gained popularity over the past several years due to its various uses in understanding human computer interaction, understanding consumers behavior, psychology, smart system, etc. Due to the massive volumes of data available via social media, which is commonly used for expressing opinions and ideas, this issue has drawn a lot of attention. To identify emotions, this research will make use of a labelled Hinglish dataset. Deep learning-based techniques are utilized to identify emotions in tweets with mixed Hindi-English coding by utilizing transformer-based models and multilingual word embeddings derived from FastText methods. Various deep learning models, such as convolutional neural networks (CNN), long short term memory (LSTM), and bi-directional long short term memory (Bi- LSTM) have been used to analyze sentiments. In comparison to other models, the convolutional neural network (CNN) achieved the highest accuracy, 75.25%.