Sentiment analysis of social media reviews has become a popular research topic in recent years. In this paper, we explore the effectiveness of machine learning classifiers for sentiment analysis of Twitter reviews. We collected a dataset of 10,000 tweets containing product reviews, and manually annotated them with their sentiment labels (positive, negative, or neutral). We then trained and evaluated six different machine learning classifiers, including Naive Bayes, Support Vector Machines (SVM), Random Forests, K-Nearest Neighbors (KNN), Decision Trees, and Logistic Regression. We used a variety of feature extraction techniques, including bag-of-words, n-grams, and word embeddings.

deep learning, sentimental analysis, support vector machines, twitter sentiment analysi