A generic lexicon-based framework for sentiment analysis. Issue 5 (3rd July 2020) - Moussa, Mohammed Elsaid; Mohamed, Ensaf Hussein; Haggag, Mohamed Hassan
ABSTRACT: On the internet, people share their opinions and sentiments in many forms of products or services. This behavior creates an extensive collection of opinions and views in the form of texts, which needs to be analyzed to know the efficacy of the product or service. Traditional lexicon-based approaches suffer from scalability and are limited by unreliable sentiment lexicons that are commonly created manually by experts. So, we proposed a generic, extendable, domain-independent lexicon-based framework for sentiment analysis that overcomes scalability, limitedness, and beats existing models in accuracy. The framework engine sums up the sentiment intensities based on a list of different lexicons, and a dedicated neutral bias module handles the misinterpreted neutral polarities. Extensive experiments on five different lexicons and using a sample sentiment engine show significant improvements in accuracy over traditional lexicon-based sentiment approaches.