Ensemble Learning Solution for the Aspect-based Sentimental Analysis on IMDB reviews

Nowadays, social media significantly influences how people form opinions about any type of business, politics, commerce, etc. based on user ratings. These reviews were examined using the field of sentiment analysis. This is a crucial component since well-designed and carried out sentiment assessments may lead to better and more accurate estimates in both business and politics. Sentiment analysis is skilled at overcoming a variety of difficulties, including issues with accuracy, problems with binary classification, problems with polarity change and data scarcity. There have been several approaches proposed and developed for this, but none of them have been effective in consistently extracting sentiment analysis. We reviewed the traditional lexicon-based method and then we developed an ensemble model employing machine learning algorithms that outperformed the lexicon-based approach by 89 percent. Additionally, we have shown through a comparison study why the suggested model is the most effective. The stacking classifier ensemble strategy that we utilized in this case allowed us to boost classification accuracy by 1% while utilizing a variety of well-known machine learning algorithms.