Implicit Feature Extraction for Sentiment Analysis in Consumer Reviews

2014 ◽  
pp. 228-231 ◽  
Author(s):  
Kim Schouten ◽  
Flavius Frasincar
Keyword(s):  
Feature Extraction ◽  
Sentiment Analysis ◽  
Consumer Reviews

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

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.

scholarly journals Sentiment Analysis on Consumer Reviews of Amazon Products

2021 ◽  
Vol 13 (2) ◽  
pp. 35-41
Author(s):  
Aamir Rashid ◽  
◽  
Ching-yu Huang
Keyword(s):  
Sentiment Analysis ◽  
Consumer Reviews

Identifying Explicit Features for Sentiment Analysis in Consumer Reviews

2014 ◽  
pp. 357-371 ◽  
Author(s):  
Nienke de Boer ◽  
Marijtje van Leeuwen ◽  
Ruud van Luijk ◽  
Kim Schouten ◽  
Flavius Frasincar ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Consumer Reviews

Feature extraction using CMIM for sentiment analysis

2019 ◽  
Vol 13 (3/4) ◽  
pp. 368
Author(s):  
B. Madhusudhanan ◽  
S. Chitra ◽  
S. Anbuchelian
Keyword(s):  
Feature Extraction ◽  
Sentiment Analysis
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