Challenges and Recommended Solutions in Multi-Source and Multi-Domain Sentiment Analysis
The massive availability of online reviews and postings in social media offers invaluable feedback for businesses to make better informed decisions in steering their marketing strategies towards users' interests and preferences. Sentiment analysis is, therefore, essential for determining the pu...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers
2019
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| _version_ | 1825722217733816320 |
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| author | Abdullah, Nor Aniza Feizollah, Ali Ainin, Sulaiman Anuar, Nor Badrul |
| author_facet | Abdullah, Nor Aniza Feizollah, Ali Ainin, Sulaiman Anuar, Nor Badrul |
| author_sort | Abdullah, Nor Aniza |
| collection | UM |
| description | The massive availability of online reviews and postings in social media offers invaluable feedback for businesses to make better informed decisions in steering their marketing strategies towards users' interests and preferences. Sentiment analysis is, therefore, essential for determining the public's opinion towards a particular topic, product or service. Traditionally, sentiment analysis is performed on a single data source, for instance, online product reviews or Tweets. However, the need to develop a more precise, and more comprehensive result has steered the move towards performing sentiment analysis on multiple data sources. The use of multiple data sources for a particular domain of interest can increase the amount of datasets needed for training a sentiment classifier. Till now, the problem of insufficient datasets for training the classifier is only addressed by multi-domain sentiment analysis. Aiming to equip researchers with a thorough understanding on both multi-source and multi-domain sentiment analysis, this paper aims to identify the underlying challenges of multi-source and multi-domain sentiment analysis, and discuss the solutions applied by the researchers concerned. This paper also offers an insightful discussion of the findings derived from past studies, and based on these, propose some useful suggestions for the future direction of this research area. Findings derived from our review would be beneficial towards guiding researchers towards the future progress and advancement of multi-source and multi-domain sentiment analysis. © 2013 IEEE. |
| first_indexed | 2024-03-06T06:02:17Z |
| format | Article |
| id | um.eprints-24270 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T06:02:17Z |
| publishDate | 2019 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | um.eprints-242702020-05-05T04:52:30Z http://eprints.um.edu.my/24270/ Challenges and Recommended Solutions in Multi-Source and Multi-Domain Sentiment Analysis Abdullah, Nor Aniza Feizollah, Ali Ainin, Sulaiman Anuar, Nor Badrul BP Islam. Bahaism. Theosophy, etc QA75 Electronic computers. Computer science The massive availability of online reviews and postings in social media offers invaluable feedback for businesses to make better informed decisions in steering their marketing strategies towards users' interests and preferences. Sentiment analysis is, therefore, essential for determining the public's opinion towards a particular topic, product or service. Traditionally, sentiment analysis is performed on a single data source, for instance, online product reviews or Tweets. However, the need to develop a more precise, and more comprehensive result has steered the move towards performing sentiment analysis on multiple data sources. The use of multiple data sources for a particular domain of interest can increase the amount of datasets needed for training a sentiment classifier. Till now, the problem of insufficient datasets for training the classifier is only addressed by multi-domain sentiment analysis. Aiming to equip researchers with a thorough understanding on both multi-source and multi-domain sentiment analysis, this paper aims to identify the underlying challenges of multi-source and multi-domain sentiment analysis, and discuss the solutions applied by the researchers concerned. This paper also offers an insightful discussion of the findings derived from past studies, and based on these, propose some useful suggestions for the future direction of this research area. Findings derived from our review would be beneficial towards guiding researchers towards the future progress and advancement of multi-source and multi-domain sentiment analysis. © 2013 IEEE. Institute of Electrical and Electronics Engineers 2019 Article PeerReviewed Abdullah, Nor Aniza and Feizollah, Ali and Ainin, Sulaiman and Anuar, Nor Badrul (2019) Challenges and Recommended Solutions in Multi-Source and Multi-Domain Sentiment Analysis. IEEE Access, 7. pp. 144957-144971. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2019.2945340 <https://doi.org/10.1109/ACCESS.2019.2945340>. https://doi.org/10.1109/ACCESS.2019.2945340 doi:10.1109/ACCESS.2019.2945340 |
| spellingShingle | BP Islam. Bahaism. Theosophy, etc QA75 Electronic computers. Computer science Abdullah, Nor Aniza Feizollah, Ali Ainin, Sulaiman Anuar, Nor Badrul Challenges and Recommended Solutions in Multi-Source and Multi-Domain Sentiment Analysis |
| title | Challenges and Recommended Solutions in Multi-Source and Multi-Domain Sentiment Analysis |
| title_full | Challenges and Recommended Solutions in Multi-Source and Multi-Domain Sentiment Analysis |
| title_fullStr | Challenges and Recommended Solutions in Multi-Source and Multi-Domain Sentiment Analysis |
| title_full_unstemmed | Challenges and Recommended Solutions in Multi-Source and Multi-Domain Sentiment Analysis |
| title_short | Challenges and Recommended Solutions in Multi-Source and Multi-Domain Sentiment Analysis |
| title_sort | challenges and recommended solutions in multi source and multi domain sentiment analysis |
| topic | BP Islam. Bahaism. Theosophy, etc QA75 Electronic computers. Computer science |
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