Support vector machine based text sentiment analysis method and device
A technology of support vector machine and sentiment analysis, applied in the field of information processing, can solve the problem of inaccurate text sentiment classification
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Embodiment 1
[0022] Embodiment 1 of the present invention provides a text sentiment analysis method based on SVM, such as figure 1 As shown, it is a schematic flow chart of the text sentiment analysis method described in Embodiment 1 of the present invention, and the method may include the following steps:
[0023] Step 101: extract each feature item in the text to be analyzed.
[0024] Wherein, the feature items generally refer to words or vocabulary with corresponding emotional tendencies in the text, such as "beautiful", "elegant" and so on.
[0025] Step 102: Calculate the feature weights of the extracted feature items, and construct a text vector corresponding to the text to be analyzed according to the extracted feature items and the feature weights of each feature item.
[0026] Step 103: Calculate the inter-class distance of each set text class, and according to the calculated inter-class distance of each set text class, select a text class with the largest inter-class distance as...
Embodiment 2
[0074] Embodiment 2 of the present invention provides an SVM-based text sentiment analysis device that can be used to implement the method described in Embodiment 1 of the present invention. Its structural diagram is as follows figure 2 shown, including:
[0075] The extraction module 21 can be used to extract each feature item in the text to be analyzed;
[0076] The construction module 22 can be used to calculate the feature weights of the extracted feature items, and construct a text vector corresponding to the text to be analyzed according to the extracted feature items and the feature weights of each feature item;
[0077] The classification module 23 can be used to calculate the inter-class distance of each set text class, and according to the calculated inter-class distance of each set text class, select a text class with the largest corresponding inter-class distance as the first-level classification, and The rest of the other text categories are used as the second-l...
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