Sentiment Analysis on Twitter feeds to establish opinion towards entities in single entity and multi-entity texts

One problem with the traditional type of binary classification, is that the sentiment output is usually in the form of ‘1’ (positive) or ‘0’ (negative) for the string of text in the tweet, regardless if there are one or more entities referred to in the text. In scenarios where one tweet can refer to multiple entities, a more fine-grained approach is needed in order to differentiate the sentiment that is associated with the individual entities.

With this in mind, the key aim of this research is to investigate how entities and their descriptor words, for example, adjectives, verbs or adverbs can be used to identify the sentiment of the tweet in relation to the entity or entities, where more than one entity exists. <br><br>This task has been approached through a hybrid approach which uses the popular sentiment lexicon – SentiWordNet 3.0 to score related descriptor words, that are within 2-word spaces of an entity, for tweets that contains more than one entity.