Sentiment Analysis Study of Human Thoughts using Machine Learning Techniques

The objective of our research is to investigate how machine learning methods can be utilized to analyze the emotions and attitudes expressed in human thinking. The study collected data from social media platforms and online forums, which included a mix of positive, negative, and neutral sentiments expressed by users. Several machine learning algorithms, including Naive Bayes, SVM, RNNs, CNNs and LSTM Networks, were employed for sentiment analysis of the data. The study found that the performance of these algorithms varied depending on the type of data being analyzed, with some algorithms performing better for short texts such as tweets, while others worked better for longer texts such as news articles. Additionally, the study found that combining multiple algorithms could improve the accuracy of sentiment analysis. According to the findings, it appears that the utilization of machine learning methods can serve as a potent means of scrutinizing human thoughts and emotions, which can have implications for a range of applications, including marketing, politics, and mental health. This article provides a comprehensive and organized review of sentiment analysis methods. The motive of the review is to analyze and categorize available techniques while comparing their strengths and weaknesses. The aim is to gain a deeper understanding of the challenges that exist in the field and to identify potential solutions and future directions. To facilitate this analysis, we also introduce several factors that can be used to evaluate the advantages and disadvantages of each method within its category.