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• W4361301311 abstract "Text mining methods usually use statistical information to solve text and language-independent procedures. Text mining methods such as polarity detection 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. In this article, a fast and efficient deterministic method for solving the problems is proposed. In the proposed method firstly we transform text and labels into a set of equations. In the second step, a mathematical solution of ill-posed equations known as Tikhonov regularization was used as a deterministic and non-probabilistic way including additional assumptions, such as smoothness of solution to assign a weight that can reflect the semantic information of each sentimental word. We confirmed the efficiency of the proposed method in 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. Moreover, we demonstrated the effectiveness of our proposed method over the most common and traditional machine learning, stochastic and fuzzy methods." @default.
• W4361301311 created "2023-03-31" @default.
• W4361301311 creator A5017647326 @default.
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• W4361301311 date "2023-03-29" @default.
• W4361301311 modified "2023-09-30" @default.
• W4361301311 title "Deterministic solution of algebraic equations in sentiment analysis" @default.
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• W4361301311 doi "https://doi.org/10.1007/s11042-023-15140-3" @default.
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• W4361301311 hasPublicationYear "2023" @default.
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