Review of "Subjective Understanding, Computer Models of Belief Systems by Jaime G. Carbonell"; UMI Research Press, Ann Arbor Michigan Copyright 1981

Carlson, Gary

doi:10.1145/1056176.1056188

Cited by 2 publications

(3 citation statements)

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“…The first documented work on opinion mining was made in 1979 at Yale University in the United States by designing a computer model of human mental understanding following the work of Carbonell [14]. It automatically determined the political views of the people of the United States and Russia.…”

Section: Literature Of Opinion Miningmentioning

confidence: 99%

“…The best performance was obtained by using the backing vector classification of singleword and two word data sets. They manually tagged 2,000 different comments on the films for training purpose [14]. Furthermore, Pang and co-workers investigated machine learning and observation techniques [45,46].…”

Section: Literature Of Opinion Miningmentioning

confidence: 99%

“…Opinion mining is a general framework for processing emotions, criticisms, suggestions and comments in different inputs such as text, video, voice, etc. Social networks such as Twitter, Facebook, or any other platforms process textual information and then categorize them using opinion mining methods [5,14,44].…”

Section: Introductionmentioning

confidence: 99%

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Deterministic solution of algebraic equations in sentiment analysis

Zahedi

Basiri

2023

Multimed Tools Appl

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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.

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Section: Literature Of Opinion Miningmentioning

confidence: 99%