Evaluation of Sentiment using Deep Learning and Machine Learning using Word Integration Techniques

Sentiment analysis involves deciphering contextual sentiments from textual data, which may originate from various sources such as social media posts, comments, reviews, blogs, tweets, and news articles. This process begins with text preprocessing techniques like stop word removal, lemmatization, and stemming, preparing the text for further analysis. Subsequently, the preprocessed text undergoes transformation into vectors via word embedding techniques, facilitating the application of machine learning and neural network methodologies. Among these, Word2vec and Skip-gram have emerged as effective supervised learning techniques, while GLoVe stands out in the unsupervised learning domain. Additionally, Recurrent Neural Networks (RNNs) and Convolution Neural Networks (CNNs) represent the deep learning approaches for analyzing textual data. This paper delves into how these machine learning and deep learning algorithms, applied within word embedding frameworks, can be instrumental in extracting features. These features can then be classified into positive, negative, or neutral sentiments, offering insights into the moral and ethical perspectives of individuals across various sectors, including business, politics, e-commerce, healthcare, and film reviews. Our findings indicate that deep learning methods surpass traditional machine learning in accuracy and effectiveness in predicting sentiments.