Survey Article on Sentiment Analysis of Social Media Posts using Machine Learning Algorithms
Keywords:
Data pre-process, Deep learning, Emotions, Feature extraction, Machine learning, Opinion mining, Sentiment analysis, Sentiment polarity, Social mediaAbstract
In the current digital age, a vast number of opinions are shared daily across online platforms such as blogs, social media, and comment sections. These opinions, often expressed in the form of reviews, feedback, and posts, have become extremely valuable for businesses, political parties, and various organisations. Understanding the sentiment behind these texts helps organisations make informed decisions, enhance customer satisfaction, shape marketing strategies, and even influence public opinions. This has led to the growing importance of sentiment analysis, also known as opinion mining, which involves identifying and extracting emotions, attitudes, and opinions from text data. This paper presents a comprehensive overview of sentiment analysis techniques, highlighting their applications, benefits, and limitations. It explores traditional machine learning approaches in depth and gives a superficial understanding of modern deep learning models, examining how data is pre-processed so the ML model can process natural language to detect sentiments. This paper also discusses the challenges involved in sentiment analysis, such as sarcasm and multilingual data. Furthermore, the review provides insights into current trends and ongoing research aimed at improving the accuracy of the sentiment analysis system. Overall, this review intends to offer a comparative understanding of the tools and techniques used in sentiment analysis using machine learning algorithms and their relevance in real-world scenarios.
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