A Hybrid Hierarchical Mathematical Heuristic Solution of Sparse Algebraic Equations in Sentiment Analysis

Many text mining methods have used statistical information as text and language-independent procedures that are not deterministic. Text mining methods based on stochastic patterns and rules need many samples to train. On the other hand, deterministic and non-probabilistic methods are easy to solve and faster than other methods but are not efficient in NLP data. This research presents a novel hybrid method to solve unbalanced, complex semi linear algebraic equation systems. The suggested method uses mathematical operators to find a good local candidate as an answer. The local candidate is used as an initial population and is given to a heuristic method (such as genetic algorithm) to find a global answer. We used this hybrid method to solve a semilinear NLP scoring scheme in a polarity detection method.We confirmed the efficiency of the proposed method on the SemEval-2013 competition, ESWC Database and Taboada database as three different cases. We observed improvement of our method over negative polarity due to our proposed mathematical step and modified goal function in the genetic algorithm. Moreover, we demonstrated the effectiveness of our proposed method over the most common and traditional machine learning, stochastic and fuzzy methods