Classifying Opinions and Sentiments on Social Networking Sites using Machine Learning Classifiers

Abstract

Abstract: People now publish evaluations on social media for any product, movie, or location they visit as a result of the Web's rapid development. Customers and product owners can both benefit from the reviews posted on social media in order to assess their offerings. Compared to unstructured data, structured data is simpler to analyze. The reviews are mostly available in an unstructured format. Aspect-Based Sentiment Analysis extracts from the reviews the features of a product and then calculates sentiment for each feature. Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique used to determine the sentiment or emotional tone expressed in a piece of text, such as a sentence, paragraph, or document. Machine leaning classifiers are used to classify sentiments. Machine learning classifiers cannot process raw text so raw text needs to be converted into vectorized form. Feature extraction techniques are used to convert raw text to numerical form also called vectorized data. In present research, four feature extraction techniques with five different machine learning classifiers namely, SVM, Logistic Regression, Naïve Bayes, Random Forest, and KNN are applied to classify sentiments associated with tweets. Two online twitter data sets containing tweets about product reviews and tweets about people's thoughts on public policy are selected for experimentation. In the experiments done, it has been found that the SVM classifier using TFIDF and HFE shows better performance as compared to other classifiers. Using the feature sets, 97% accuracy and 98% F1- score is achieved in the aspect category prediction task.