(untitled) (Contemporary Journal of Social Science Review, 2024)
This study investigates sentiment analysis using natural language processing (NLP) techniques and machine learning algorithms. A diverse dataset, comprising approximately 50,000 textual entries from social media, product reviews, and news articles, was collected and analyzed. The research employed preprocessing techniques such as tokenization and stemming, alongside feature extraction methods like Bag-of-Words and Term Frequency-Inverse Document Frequency (TF-IDF). Various machine learning models, including Support Vector Machines, Random Forests, and Long Short-Term Memory networks, were evaluated for their effectiveness in classifying sentiments as positive, negative, or neutral. Results indicated that the LSTM model outperformed other algorithms, achieving an accuracy of 90%. This study highlights the complexities of sentiment classification, particularly in handling nuanced expressions, and underscores the potential for future advancements in NLP and machine learning methodologies.