A SEMI-SUPERVISED FRAMEWORK BASED ON SELF-CONSTRUCTED ADAPTIVE LEXICON FOR PERSIAN SENTIMENT ANALYSIS

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NAJAFZADEH MOHSEN; Rahati quchani Saeed; GHAEMI REZA

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Journal: SIGNAL AND DATA PROCESSING Year:2018 | Volume:15 | Issue:2 (SERIAL 36) Page(s): 89-101

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Information Journal Paper

Title

A SEMI-SUPERVISED FRAMEWORK BASED ON SELF-CONSTRUCTED ADAPTIVE LEXICON FOR PERSIAN SENTIMENT ANALYSIS

Author(s)

NAJAFZADEH MOHSEN | Rahati quchani Saeed | GHAEMI REZA | Issue Writer Certificate

Keywords

OPINION MININGQ1

SELF-TRAININGQ1

SELF-CONSTRUCTED LEXICONQ1

HIDDEN MARKOV MODELQ2

ADAPTIVE DICTIONARYQ1

Abstract

With the appearance of Web 2. 0 and 3. 0, users’ contribution to WWW has created a huge amount of valuable expressed opinions. Considering the difficulty or impossibility of manually analyzing such big data, sentiment analysis, as a branch of natural language processing, has been highly considered. Despite the other (popular) languages, a limited number of research studies have been conducted in Persian sentiment analysis. In this study, for the first time, a semi-supervised framework is proposed for Persian sentiment analysis. Moreover, considering that one of the most recent studies in Persian, is an algorithm based on extracting adaptive (dataset-sensitive) expert-based emotional patterns. In this research, extraction of the same state-of-the-art emotional patterns is proposed to be performed automatically. Moreover, application of the HMM classifier, by utilizing the mentioned features (as its states) is analyzed; and additionally, HMM-based sentiment analysis is upgraded by being combined with a rule-based classifier for the opinion assignment process. In addition, toward intelligent SELF-TRAINING, a criterion for evaluating, the high reliability of output is presented by which (assuming satisfaction of the criterion) the SELF-TRAINING process is performed in “ lexicon-extraction” and “ classifier, ” as learning systems. The proposed method, by being applied on the basis dataset, provides 90% of accuracy (despite its expert-independent lexicon generation nature), which in comparison with the supervised and semi-supervised methods in the state-of-the-art has a considerable superiority. Moreover, this semi-supervised method is evaluated by a 10/90 ratio of train/ test and its reliability is demonstrated by providing 80% of accuracy.

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APA: Copy

NAJAFZADEH, MOHSEN, Rahati quchani, Saeed, & GHAEMI, REZA. (2018). A SEMI-SUPERVISED FRAMEWORK BASED ON SELF-CONSTRUCTED ADAPTIVE LEXICON FOR PERSIAN SENTIMENT ANALYSIS. SIGNAL AND DATA PROCESSING, 15(2 (SERIAL 36) ), 89-101. SID. https://sid.ir/paper/160927/en

Vancouver: Copy

NAJAFZADEH MOHSEN, Rahati quchani Saeed, GHAEMI REZA. A SEMI-SUPERVISED FRAMEWORK BASED ON SELF-CONSTRUCTED ADAPTIVE LEXICON FOR PERSIAN SENTIMENT ANALYSIS. SIGNAL AND DATA PROCESSING[Internet]. 2018;15(2 (SERIAL 36) ):89-101. Available from: https://sid.ir/paper/160927/en

IEEE: Copy

MOHSEN NAJAFZADEH, Saeed Rahati quchani, and REZA GHAEMI, “A SEMI-SUPERVISED FRAMEWORK BASED ON SELF-CONSTRUCTED ADAPTIVE LEXICON FOR PERSIAN SENTIMENT ANALYSIS,” SIGNAL AND DATA PROCESSING, vol. 15, no. 2 (SERIAL 36) , pp. 89–101, 2018, [Online]. Available: https://sid.ir/paper/160927/en

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