Impact of Double Negation through Majority Voting of Machine Learning Algorithms

Yadav Pinky, Kashyap Indu, Bhoopesh Singh Bhati

doi:10.5109/7172289

Sentiment analysis, a subfield of natural language processing (NLP), has grown significantly in importance and complexity. This research introduces an innovative framework for handling binary clustered sentences, a prevalent challenge in sentiment analysis. This approach groups sentences into positive or negative clusters and determines the sentiment of each cluster based on the majority of sentences within it, enhancing the overall accuracy of sentiment analysis. Another overlooked yet crucial aspect, the impact of negation and double negation on sentiment polarity, is also addressed. Current models often fail to capture these linguistic nuances, hindering a complete understanding of the true sentiment in the text. The research also introduces the FFBC algorithm, specifically designed to handle complex linguistic constructs like negations and double negations, often overlooked in current models. Validated on IMDb and Amazon Reviews Datasets, and tested on a unique Farmers' Protest Twitter dataset, the framework shows enhanced performance across key metrics compared to leading techniques like BERT, LSTMs, VADER, and SVM. This improvement underscores the potential of advanced sentiment analysis techniques in the digital era, offering significant insights into public sentiment during global events. The study concludes by highlighting the implications of this research for various stakeholders and outlining future research directions.

Impact of Double Negation through Majority Voting of Machine Learning Algorithms

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Impact of Double Negation through Majority Voting of Machine Learning Algorithms

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