Introduction: Sentiment analysis is the process of determining the emotion or sentiment expressed in a piece of text, such as a tweet, review, or article. With the growing availability of user-generated content on social media platforms, sentiment analysis has become an important area of research in the field of machine learning. Various machine learning algorithms have been developed to perform sentiment analysis, each with its own strengths and weaknesses. This article presents a comparative study of some of the most commonly used machine learning algorithms for sentiment analysis.
Methodology: To compare the performance of different machine learning algorithms, we used a dataset of tweets collected from Twitter using the Twitter API. The dataset consisted of tweets from four categories: positive, negative, neutral, and mixed. We preprocessed the dataset by removing stopwords, punctuation, and special characters, and performed tokenization and stemming. We then used five different machine learning algorithms: Naive Bayes, Logistic Regression, Support Vector Machine (SVM), Random Forest, and Multilayer Perceptron (MLP) to classify the tweets into their respective sentiment categories.
Results: The results showed that all of the machine learning algorithms performed well in classifying the tweets into the positive and negative categories, with accuracy rates ranging from 85% to 95%. However, the performance of the algorithms varied in the classification of neutral and mixed tweets. Naive Bayes and Logistic Regression had the highest accuracy rates for classifying neutral tweets, while SVM and Random Forest had the highest accuracy rates for classifying mixed tweets. MLP had the lowest accuracy rate for all four categories.
Discussion: The results of this study suggest that the choice of machine learning algorithm for sentiment analysis depends on the specific task and the types of text being analyzed. Naive Bayes and Logistic Regression are good choices for analyzing neutral sentiment, while SVM and Random Forest are better suited for analyzing mixed sentiment. However, for overall sentiment analysis, Random Forest performed the best among all the algorithms tested.
Conclusion: Sentiment analysis is an important area of research in machine learning, and various algorithms have been developed to perform this task. This comparative study provides valuable insights into the performance of different machine learning algorithms for sentiment analysis, and can help researchers and practitioners in selecting the most appropriate algorithm for their specific needs.