What are the weaknesses of SVM for sentiment analysis?
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Support Vector Machine (SVM) has some weaknesses in sentiment analysis. One weakness is the parameter selection, which can affect the accuracy of the model . Another weakness is that SVM sacrifices the distribution of data, which can lead to lower accuracy and stability when dealing with big and complex sentiment data . Additionally, SVM can be slower in processing speed compared to other methods like Long Short-term Memory (LSTM) in deep learning .
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| Papers (3) | Insight |
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| The weaknesses of SVM for sentiment analysis include sacrificing the distribution of data and resulting in lower accuracy and stability. | |
23 Sep 2022 | The weakness of SVM for sentiment analysis is that it has lower accuracy compared to LSTM but slower processing speed. |
| The weaknesses of SVM for sentiment analysis include the need for parameter selection and potential for lower accuracy. |
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