Research on the Sentiment Analysis Based on Machine Learning and Feature Extraction Algorithm

2020 ◽

pp. 291-296

Author(s):

Sulis Sandiwarno

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Learning Systems ◽

Experimental Results ◽

Extraction Techniques ◽

Machine Learning Classifiers ◽

Feature Extraction Algorithm ◽

Extraction Algorithm ◽

E Learning ◽

Hybrid Feature Extraction

In order to solve some problems of importance of words and missing relations of semantic between words in the emotional analysis of e-learning systems, the TF-IWF algorithm weighted Word2vec algorithm model was proposed as a feature extraction algorithm. Moreover, to support this study, we employ Multinomial Naïve Bayes (MNB) to obtain more accurate results. There are three mainly steps, firstly, TF-IWF is employed used to compute the weight of word. Second, Word2vec algorithm is adopted to compute the vector of words, Third, we concatenate first and second steps. Finally, the users' opinions data is trained and classified through several machine learning classifiers especially MNB classifier. The experimental results indicate that the proposed method outperformed against previous approaches in terms of precision, recall, F-Score, and accuracy.

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Iris feature extraction algorithm based on odd symmetry 2D Log-Gabor

Journal of Computer Applications ◽

10.3724/sp.j.1087.2009.00976 ◽

2009 ◽

Vol 29 (4) ◽

pp. 976-978 ◽

Cited By ~ 1

Author(s):

Lin-tao Lü ◽

Tao YANG

Keyword(s):

Feature Extraction ◽

Feature Extraction Algorithm ◽

Extraction Algorithm ◽

Iris Feature Extraction ◽

Log Gabor

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Corresponding Feature Extraction Algorithm between Infrared and Visible Images Using MSER

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY ◽

10.3724/sp.j.1146.2010.01111 ◽

2011 ◽

Vol 33 (7) ◽

pp. 1625-1631 ◽

Cited By ~ 1

Author(s):

Lin Lian ◽

Guo-hui Li ◽

Hai-tao Wang ◽

hao Tian ◽

Shu-kui Xu

Keyword(s):

Feature Extraction ◽

Feature Extraction Algorithm ◽

Extraction Algorithm ◽

Visible Images

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Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

Kinetik Game Technology Information System Computer Network Computing Electronics and Control ◽

10.22219/kinetik.v5i3.1066 ◽

2020 ◽

pp. 235-242

Author(s):

Farrikh Alzami ◽

Erika Devi Udayanti ◽

Dwi Puji Prabowo ◽

Rama Aria Megantara

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Random Forest ◽

Sentiment Analysis ◽

Classification Performance ◽

Document Preparation ◽

Learning Models ◽

Polarity Classification ◽

Negative Sentiment ◽

Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

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