FastText Word Embedding and Random Forest Classifier for User Feedback Sentiment Classification in Bahasa Indonesia Article Swipe
Yehezkiel Gunawan
,
Julio Christian Young
,
Andre Rusli
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.31937/ti.v13i2.2124
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.31937/ti.v13i2.2124
User feedback nowadays become a platform for software developer to identify and understand user requirements, preferences, and user's complaints. It is important for the developer to identify the problem that exist in user feedback. According to software growth, user amount also growth. Read and classify one by one manually are wasting time and energy. As the solution for the problem, sentiment analysis system using Random Forest Classifier which use word embedding as the feature extraction is made to help to classify which feedback is positive, neutral, or negative. Random Forest Algorithm is chosen because it gives the best performance, even its need the larger resources. Furthermore, with word embedding, the words which has semantic or syntactic similarities will be detected. Word embedding does not need stemming and stop word removal, so the context of the sentences keep remains. This research is made to implement word embedding to classify sentiment of user feedbacks using Random Forest Classifier. 70.27% accuracy, 80% precision, 54 recall and 54% F1 score is reached when BYU dataset (200 dimension) as embedding dataset with the train and test ratio 80:20.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.31937/ti.v13i2.2124
- https://ejournals.umn.ac.id/index.php/TI/article/download/2124/1212
- OA Status
- diamond
- Cited By
- 2
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4220998451
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4220998451Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.31937/ti.v13i2.2124Digital Object Identifier
- Title
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FastText Word Embedding and Random Forest Classifier for User Feedback Sentiment Classification in Bahasa IndonesiaWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-23Full publication date if available
- Authors
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Yehezkiel Gunawan, Julio Christian Young, Andre RusliList of authors in order
- Landing page
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https://doi.org/10.31937/ti.v13i2.2124Publisher landing page
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https://ejournals.umn.ac.id/index.php/TI/article/download/2124/1212Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ejournals.umn.ac.id/index.php/TI/article/download/2124/1212Direct OA link when available
- Concepts
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Word embedding, Computer science, Random forest, Sentiment analysis, Classifier (UML), Embedding, Word (group theory), Natural language processing, Artificial intelligence, Software, Machine learning, Mathematics, Programming language, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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16Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.dataset | 170, 175 |
| abstract_inverted_index.energy. | 53 |
| abstract_inverted_index.feature | 73 |
| abstract_inverted_index.growth, | 37 |
| abstract_inverted_index.growth. | 41 |
| abstract_inverted_index.problem | 28 |
| abstract_inverted_index.reached | 167 |
| abstract_inverted_index.wasting | 50 |
| abstract_inverted_index.analysis | 61 |
| abstract_inverted_index.classify | 44, 80, 147 |
| abstract_inverted_index.feedback | 1, 82 |
| abstract_inverted_index.identify | 10, 26 |
| abstract_inverted_index.manually | 48 |
| abstract_inverted_index.neutral, | 85 |
| abstract_inverted_index.nowadays | 2 |
| abstract_inverted_index.platform | 5 |
| abstract_inverted_index.problem, | 59 |
| abstract_inverted_index.remains. | 137 |
| abstract_inverted_index.removal, | 129 |
| abstract_inverted_index.research | 139 |
| abstract_inverted_index.semantic | 113 |
| abstract_inverted_index.software | 7, 36 |
| abstract_inverted_index.solution | 56 |
| abstract_inverted_index.stemming | 125 |
| abstract_inverted_index.According | 34 |
| abstract_inverted_index.Algorithm | 90 |
| abstract_inverted_index.accuracy, | 157 |
| abstract_inverted_index.detected. | 119 |
| abstract_inverted_index.developer | 8, 24 |
| abstract_inverted_index.embedding | 70, 121, 145, 174 |
| abstract_inverted_index.feedback. | 33 |
| abstract_inverted_index.feedbacks | 151 |
| abstract_inverted_index.implement | 143 |
| abstract_inverted_index.important | 21 |
| abstract_inverted_index.negative. | 87 |
| abstract_inverted_index.positive, | 84 |
| abstract_inverted_index.sentences | 135 |
| abstract_inverted_index.sentiment | 60, 148 |
| abstract_inverted_index.syntactic | 115 |
| abstract_inverted_index.Classifier | 66 |
| abstract_inverted_index.dimension) | 172 |
| abstract_inverted_index.embedding, | 108 |
| abstract_inverted_index.extraction | 74 |
| abstract_inverted_index.precision, | 159 |
| abstract_inverted_index.resources. | 104 |
| abstract_inverted_index.understand | 12 |
| abstract_inverted_index.Classifier. | 155 |
| abstract_inverted_index.complaints. | 18 |
| abstract_inverted_index.Furthermore, | 105 |
| abstract_inverted_index.performance, | 98 |
| abstract_inverted_index.preferences, | 15 |
| abstract_inverted_index.similarities | 116 |
| abstract_inverted_index.requirements, | 14 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.7200000286102295 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.70960001 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |