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Sentiment Analysis or Opinion Mining: A Review

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@article{IJASEIT2137,
   author = {Saidah Saad and Bilal Saberi},
   title = {Sentiment Analysis or Opinion Mining: A Review},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {5},
   year = {2017},
   pages = {1660--1666},
   keywords = {opinion mining; sentiment analysis; NLP; machine learning; lexical-based},
   abstract = {Opinion Mining (OM) or Sentiment Analysis (SA) can be defined as the task of detecting, extracting and classifying opinions on something. It is a type of the processing of the natural language (NLP) to track the public mood to a certain law, policy, or marketing, etc. It involves a way that development for the collection and examination of comments and opinions about legislation, laws, policies, etc., which are posted on the social media. The process of information extraction is very important because it is a very useful technique but also a challenging task. That mean, to extract sentiment from an object in the web-wide, need to automate opinion-mining systems to do it. The existing techniques for sentiment analysis include machine learning (supervised and unsupervised), and lexical-based approaches. Hence, the main aim of this paper presents a survey of sentiment analysis (SA) and opinion mining (OM) approaches, various techniques used that related in this field. As well, it discusses the application areas and challenges for sentiment analysis with insight into the past researcher's works.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2137},
   doi = {10.18517/ijaseit.7.5.2137}
}

EndNote

%A Saad, Saidah
%A Saberi, Bilal
%D 2017
%T Sentiment Analysis or Opinion Mining: A Review
%B 2017
%9 opinion mining; sentiment analysis; NLP; machine learning; lexical-based
%! Sentiment Analysis or Opinion Mining: A Review
%K opinion mining; sentiment analysis; NLP; machine learning; lexical-based
%X Opinion Mining (OM) or Sentiment Analysis (SA) can be defined as the task of detecting, extracting and classifying opinions on something. It is a type of the processing of the natural language (NLP) to track the public mood to a certain law, policy, or marketing, etc. It involves a way that development for the collection and examination of comments and opinions about legislation, laws, policies, etc., which are posted on the social media. The process of information extraction is very important because it is a very useful technique but also a challenging task. That mean, to extract sentiment from an object in the web-wide, need to automate opinion-mining systems to do it. The existing techniques for sentiment analysis include machine learning (supervised and unsupervised), and lexical-based approaches. Hence, the main aim of this paper presents a survey of sentiment analysis (SA) and opinion mining (OM) approaches, various techniques used that related in this field. As well, it discusses the application areas and challenges for sentiment analysis with insight into the past researcher's works.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2137
%R doi:10.18517/ijaseit.7.5.2137
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 5
%@ 2088-5334

IEEE

Saidah Saad and Bilal Saberi,"Sentiment Analysis or Opinion Mining: A Review," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 5, pp. 1660-1666, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.5.2137.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Saad, Saidah
AU  - Saberi, Bilal
PY  - 2017
TI  - Sentiment Analysis or Opinion Mining: A Review
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 5
Y2  - 2017
SP  - 1660
EP  - 1666
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - opinion mining; sentiment analysis; NLP; machine learning; lexical-based
N2  - Opinion Mining (OM) or Sentiment Analysis (SA) can be defined as the task of detecting, extracting and classifying opinions on something. It is a type of the processing of the natural language (NLP) to track the public mood to a certain law, policy, or marketing, etc. It involves a way that development for the collection and examination of comments and opinions about legislation, laws, policies, etc., which are posted on the social media. The process of information extraction is very important because it is a very useful technique but also a challenging task. That mean, to extract sentiment from an object in the web-wide, need to automate opinion-mining systems to do it. The existing techniques for sentiment analysis include machine learning (supervised and unsupervised), and lexical-based approaches. Hence, the main aim of this paper presents a survey of sentiment analysis (SA) and opinion mining (OM) approaches, various techniques used that related in this field. As well, it discusses the application areas and challenges for sentiment analysis with insight into the past researcher's works.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2137
DO  - 10.18517/ijaseit.7.5.2137

RefWorks

RT Journal Article
ID 2137
A1 Saad, Saidah
A1 Saberi, Bilal
T1 Sentiment Analysis or Opinion Mining: A Review
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 5
YR 2017
SP 1660
OP 1666
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 opinion mining; sentiment analysis; NLP; machine learning; lexical-based
AB Opinion Mining (OM) or Sentiment Analysis (SA) can be defined as the task of detecting, extracting and classifying opinions on something. It is a type of the processing of the natural language (NLP) to track the public mood to a certain law, policy, or marketing, etc. It involves a way that development for the collection and examination of comments and opinions about legislation, laws, policies, etc., which are posted on the social media. The process of information extraction is very important because it is a very useful technique but also a challenging task. That mean, to extract sentiment from an object in the web-wide, need to automate opinion-mining systems to do it. The existing techniques for sentiment analysis include machine learning (supervised and unsupervised), and lexical-based approaches. Hence, the main aim of this paper presents a survey of sentiment analysis (SA) and opinion mining (OM) approaches, various techniques used that related in this field. As well, it discusses the application areas and challenges for sentiment analysis with insight into the past researcher's works.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2137
DO  - 10.18517/ijaseit.7.5.2137