A Comparative Analysis of Natural Language Processing Techniques for Sentiment Analysis
Keywords:
actionable, practitioners, Long Short-Term Memory, Support Vector MachinesAbstract
Sentiment analysis, a crucial subfield of Natural Language Processing (NLP), focuses on discerning the sentiment or emotional tone behind a body of text. Given the exponential growth of text data from social media, customer reviews, and various online platforms, effective sentiment analysis techniques are vital for extracting meaningful insights. This paper presents a comparative analysis of various NLP techniques employed for sentiment analysis, including traditional methods such as bag-of-words and TF-IDF, advanced machine learning approaches like Support Vector Machines (SVM) and Naive Bayes, and cutting-edge deep learning techniques like Long Short-Term Memory (LSTM) networks and Bidirectional Encoder Representations from Transformers (BERT). By evaluating their performance based on accuracy, computational efficiency, and applicability to diverse contexts, this study aims to identify the strengths and weaknesses of each approach and provide actionable recommendations for practitioners.
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