Deterministic solution of algebraic equations in sentiment analysis.

Area of Science:

• Natural Language Processing
• Computational Linguistics
• Machine Learning

Background:

• Traditional text mining often relies on statistical methods requiring extensive training data.
• Stochastic approaches for tasks like sentiment analysis can be sample-intensive and computationally expensive.
• Existing deterministic methods may lack efficiency for complex Natural Language Processing (NLP) data.

Purpose of the Study:

• To propose a fast and efficient deterministic text mining method for NLP tasks.
• To address the limitations of stochastic and traditional machine learning methods in text analysis.
• To enhance sentiment analysis by incorporating semantic information through a novel mathematical approach.

Main Methods:

• Transforming text and labels into a system of equations.
• Applying Tikhonov regularization, a mathematical solution for ill-posed equations.
• Assigning weights to sentimental words based on solution smoothness to capture semantic meaning.

Main Results:

• Validated the method's efficiency across SemEval-2013, ESWC Database, and Taboada database.
• Observed significant improvement in handling negative polarity compared to baseline methods.
• Demonstrated superior effectiveness against common machine learning, stochastic, and fuzzy techniques.

Conclusions:

• The proposed deterministic method offers a fast and efficient alternative for text mining and sentiment analysis.
• Tikhonov regularization provides a robust mathematical framework for extracting semantic information from text.
• The method shows promise for improving the performance of NLP tasks, particularly sentiment analysis.