Arabic Sentiment Analysis using Machine Learning
Sentiment Analysis is a rising field that is gaining popularity every day due to its importance in mining the public opinions, the immense amount of generated data every second over the Internet via social network, microblogs, blogs, forums, consumer websites and other presents a rich field of opini...
محفوظ في:
| المؤلف الرئيسي: | |
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| منشور في: |
2016
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://bspace.buid.ac.ae/handle/1234/1501 |
| الوسوم: |
إضافة وسم
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| _version_ | 1862980611697803264 |
|---|---|
| author | ATIYAH, SASI FUAD |
| author_facet | ATIYAH, SASI FUAD |
| author_role | author |
| dc.creator.none.fl_str_mv | ATIYAH, SASI FUAD |
| dc.date.none.fl_str_mv | 2016-09 2019-10-30T06:16:52Z 2019-10-30T06:16:52Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 2013128082 https://bspace.buid.ac.ae/handle/1234/1501 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | The British University in Dubai (BUiD) |
| dc.subject.none.fl_str_mv | Arabic sentiment analysis machine learning Arabic language |
| dc.title.none.fl_str_mv | Arabic Sentiment Analysis using Machine Learning |
| dc.type.none.fl_str_mv | Dissertation |
| description | Sentiment Analysis is a rising field that is gaining popularity every day due to its importance in mining the public opinions, the immense amount of generated data every second over the Internet via social network, microblogs, blogs, forums, consumer websites and other presents a rich field of opinions that are ready to be populated, aggregated and summarized and based on that decision are made. The applications are wide from the classical problems like political campaigns, product reviews to more sophisticated usage in Human Machine Interaction where the detection of the human sentiment plays an important role in a successful machine interaction. In this research we investigated the problem of sentiment analysis in the Arabic language and focus on how to utilize the machine learning-based approach to its maximum by conducting several experiments on several multi-domain dataset and optimize the trained model using parameter optimization and using the findings to establish a predefined best parameter settings to be used on new datasets. The research showed that through parameter optimization, basic machine learning classifiers achieved higher results than other more complex hybrid approaches, in addition, the overall parameters settings were tested on two new datasets and provided very promising results indicating that performance weren’t as a cause of overfitting. The research also explains the issues of testing such well-trained models on an unseen dataset from different sources in the same domain and how it can be solved. The work was concluded by the possible enhancements that can be applied to the work done and a new path for future work that promises a more generalized solution. |
| id | budr_b49d38347a7d64603240b9fe2f245d2c |
| identifier_str_mv | 2013128082 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/1501 |
| publishDate | 2016 |
| publisher.none.fl_str_mv | The British University in Dubai (BUiD) |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Arabic Sentiment Analysis using Machine LearningATIYAH, SASI FUADArabic sentiment analysismachine learningArabic languageSentiment Analysis is a rising field that is gaining popularity every day due to its importance in mining the public opinions, the immense amount of generated data every second over the Internet via social network, microblogs, blogs, forums, consumer websites and other presents a rich field of opinions that are ready to be populated, aggregated and summarized and based on that decision are made. The applications are wide from the classical problems like political campaigns, product reviews to more sophisticated usage in Human Machine Interaction where the detection of the human sentiment plays an important role in a successful machine interaction. In this research we investigated the problem of sentiment analysis in the Arabic language and focus on how to utilize the machine learning-based approach to its maximum by conducting several experiments on several multi-domain dataset and optimize the trained model using parameter optimization and using the findings to establish a predefined best parameter settings to be used on new datasets. The research showed that through parameter optimization, basic machine learning classifiers achieved higher results than other more complex hybrid approaches, in addition, the overall parameters settings were tested on two new datasets and provided very promising results indicating that performance weren’t as a cause of overfitting. The research also explains the issues of testing such well-trained models on an unseen dataset from different sources in the same domain and how it can be solved. The work was concluded by the possible enhancements that can be applied to the work done and a new path for future work that promises a more generalized solution.The British University in Dubai (BUiD)2019-10-30T06:16:52Z2019-10-30T06:16:52Z2016-09Dissertationapplication/pdf2013128082https://bspace.buid.ac.ae/handle/1234/1501enoai:bspace.buid.ac.ae:1234/15012021-09-27T11:59:15Z |
| spellingShingle | Arabic Sentiment Analysis using Machine Learning ATIYAH, SASI FUAD Arabic sentiment analysis machine learning Arabic language |
| title | Arabic Sentiment Analysis using Machine Learning |
| title_full | Arabic Sentiment Analysis using Machine Learning |
| title_fullStr | Arabic Sentiment Analysis using Machine Learning |
| title_full_unstemmed | Arabic Sentiment Analysis using Machine Learning |
| title_short | Arabic Sentiment Analysis using Machine Learning |
| title_sort | Arabic Sentiment Analysis using Machine Learning |
| topic | Arabic sentiment analysis machine learning Arabic language |
| url | https://bspace.buid.ac.ae/handle/1234/1501 |