A Literature Review on Cross Domain Sentiment Analysis Using Machine learning

Sentiment analysis is the field of NLP which analyzes the sentiments of text written by users on online sites in the form of reviews. These reviews may be either in the form of a word, sentence, document, or ratings. These reviews are used as datasets when applied to train a classifier. These datasets are applied in the annotated form with the positive, negative or neutral labels as an input to train the classifier. This trained classifier is used to test other reviews, either in the same or different domains to know like or dislike of the user for the related field. Various researches have been done in single and cross domain sentiment analysis. The new methods proposed are overcoming the previous ones but according to this survey, no methods best suit the proposed work. In this article, the authors review the methods and techniques that are given by various researchers in cross domain sentiment analysis and how those are compared with the pre-existing methods for the related work.

Download Full-text

Evaluating cross domain sentiment analysis using supervised machine learning techniques

2017 Intelligent Systems Conference (IntelliSys) ◽

10.1109/intellisys.2017.8324369 ◽

2017 ◽

Cited By ~ 2

Author(s):

Azwa Abdul Aziz ◽

Andrew Starkey ◽

Marcus Campbell Bannerman

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Cross Domain ◽

Learning Techniques

Download Full-text

Cross Domain Sentiment Analysis Using Different Machine Learning Techniques

Advances in Intelligent Systems and Computing - Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015) ◽

10.1007/978-3-319-27212-2_7 ◽

2015 ◽

pp. 77-87 ◽

Cited By ~ 3

Author(s):

S. Mahalakshmi ◽

E. Sivasankar

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Machine Learning Techniques ◽

Cross Domain ◽

Learning Techniques

Download Full-text

Approaches to Cross-Domain Sentiment Analysis: A Systematic Literature Review

IEEE Access ◽

10.1109/access.2017.2690342 ◽

2017 ◽

Vol 5 ◽

pp. 16173-16192 ◽

Cited By ~ 33

Author(s):

Tareq Al-Moslmi ◽

Nazlia Omar ◽

Salwani Abdullah ◽

Mohammed Albared

Keyword(s):

Literature Review ◽

Sentiment Analysis ◽

Systematic Literature Review ◽

Cross Domain

Download Full-text

Cross-domain sentiment analysis model on Indonesian YouTube comment

International Journal of Advances in Intelligent Informatics ◽

10.26555/ijain.v7i1.554 ◽

2021 ◽

Vol 7 (1) ◽

pp. 12

Author(s):

Agus Sasmito Aribowo ◽

Halizah Basiron ◽

Noor Fazilla Abd Yusof ◽

Siti Khomsah

Keyword(s):

Machine Learning ◽

Random Forest ◽

Sentiment Analysis ◽

Machine Learning Method ◽

Learning Method ◽

Analysis Model ◽

Language Form ◽

Cross Domain ◽

Ensemble Machine Learning ◽

Stop Word

A cross-domain sentiment analysis (CDSA) study in the Indonesian language and tree-based ensemble machine learning is quite interesting. CDSA is useful to support the labeling process of cross-domain sentiment and reduce any dependence on the experts; however, the mechanism in the opinion unstructured by stop word, language expressions, and Indonesian slang words is unidentified yet. This study aimed to obtain the best model of CDSA for the opinion in Indonesia language that commonly is full of stop words and slang words in the Indonesian dialect. This study was purposely to observe the benefits of the stop words cleaning and slang words conversion in CDSA in the Indonesian language form. It was also to find out which machine learning method is suitable for this model. This study started by crawling five datasets of the comments on YouTube from 5 different domains. The dataset was copied into two groups: the dataset group without any process of stop word cleaning and slang word conversion and the dataset group to stop word cleaning and slang word conversion. CDSA model was built for each dataset group and then tested using two types of tree-based ensemble machine learning, i.e., Random Forest (RF) and Extra Tree (ET) classifier, and tested using three types of non-ensemble machine learning, including Naïve Bayes (NB), SVM, and Decision Tree (DT) as the comparison. Then, It can be suggested that the accuracy of CDSA in Indonesia Language increased if it still removed the stop words and converted the slang words. The best classifier model was built using tree-based ensemble machine learning, particularly ET, as in this study, the ET model could achieve the highest accuracy by 91.19%. This model is expected to be the CDSA technique alternative in the Indonesian language.

Download Full-text