Use the Literature review abstract below to write a complete research paper.

Assignment Question

Use the Literature review abstract below to write a complete research paper.

Literature review abstract: In the domain of sentiment analysis, traditional Machine Learning approaches often struggle to capture the full subtleties of social media text, hindering semantic comprehension. This research introduces a novel hybrid methodology that blends knowledge graphs and deep learning for sentiment classification, specifically targeting short-form content such as Twitter posts. The approach transforms tweets into graph structures, applies graph similarity metrics, and utilizes deep learning algorithms for sentiment prediction. A key aspect of this approach is the incorporation of Local Interpretable Model- agnostic Explanations (LIME) to enhance interpretability and transparency in the decision-making process. This multi-phase methodology encompasses preprocessing, graph construction, dimensionality reduction, graph similarity analysis, sentiment prediction, and interpretability. Concurrently, in human- computer dialogues, this study presents an innovative method for detecting and analyzing emotional breakdowns, which are often overlooked by conventional dialogue breakdown detection methods. Experimental results substantiate the effectiveness of this approach in dialogue breakdown detection, surpassing methods relying solely on sentence-distributed representation vectors. Lastly, in the era of evolving Artificial Intelligence (AI), concerns regarding algorithmic bias and opacity are addressed through innovative methods that visualize AI system results. These approaches, when applied to sentiment analysis, enhance transparency and understanding. The experimental findings confirm the superiority of this system over contemporary counterparts, demonstrating its capacity for enhanced interpretability. This research advances sentiment analysis, human-computer dialogue understanding, and AI transparency, ultimately improving our ability to decipher sentiments, emotions, and decisions within textual data