Sentiment Analysis of Twitter Feeds, Effect of Feature Hashing on Model Accuracy
2018
In this work, we analyse twitter data using support vector machine algorithm to classify tweets into positive, negative and neutral sentiments. This research try to find the relationship between feature hash bit size that is used and the effect on the accuracy and precision of the model that is generated. We measure the effect of varying the feature hash bit size on the accuracy and precision of the model. The research showed that as the feature hash bit size increases at a certain point the accuracy and precision value started decreasing with increase in the feature hash bit size.