A hybrid approach to sentiment analysis of news comments

Today, the web hosts quite a voluminous amount of information. Among such information is user generated content which plays an important role in analyzing different business aspects. Sentiment analysis therefore becomes an effective way of understanding public opinions. Businesses, particularly in ecommerce, stock market, social networks and also political entities can use sentiment analysis for decision making. Traditional methods of opinion gathering involved the use of questioners and interviews which solely depend on the good will of the people to be interviewed. Most research on sentiment analysis focused on social networks, product reviews and also on the stock market. Less research has been covered on analysis of news comments. This research embarks on a hybrid approach to sentiment analysis of news comments which involves using sentiment lexicon for polarity detection (polarity will be classified as positive, negative and neutral). The results from the lexicon based method are then used to train machine learning algorithms. Two algorithms employed in this research are the Support Vector Machine (SVM) and K-Nearest Neighbour (kNN). Experimental results show that SVM performs better than kNN on news comments.