Certain investigations on sentiment analysis in social media content using machine learning and deep learning models

Abstract

Massive volumes of unstructured data are generated online by newlinesocial media platforms, and their users needs are constantly growing. The newlinepolarity of this unstructured text data is examined using machine learning and newlinedeep learning approaches in the form of text recognition. Text mining newlineresearch focuses on sentiment analysis (SA). This method recognizes newlinesubjectivity, feelings, and views in text. From regularly utilized sources like newlinewebsites and micro-blogs, text analytics and natural language processing newlinetechniques are used to identify and extract subjective information. newlineFinding sentiment scores based on reviews is the main goal of newlinesentiment analysis. Since most reviews are naturally unstructured, providing newlineuseful information for future use requires processing, such as classification or newlineclustering. Natural language processing is crucial for comprehending people s newlineemotions. However, if people use figurative language such as sarcasm, newlineeuphemism, oxymoron, and pleonasm when commenting, updating their newlinestatus, or writing movie or product reviews, this perspective may be skewed newlineand inaccurate. newlineThe main objective of the first work in this research is to newlineinvestigate how euphemistic and dysphemistic phrases are distributed newlinethroughout the news corpus various categories. For analysis of euphemism newlineand dysphemism in news, machine learning and deep learning classifier newlinemodels are developed using a new euphemistic balanced data set. To choose newlinethe most reliable classifier model for making accurate predictions across a newlinebalanced euphemistic dataset, N-gram features (Unigram, Bigram, and newlineTrigram) and three feature weighting schemes including binary occurrence, newlineterm occurrence, and term frequency-inverse document frequency (TF-IDF) newlineare employed on the machine learning classifier models including J48 newline