【目的/意義】隨著旅游網站的增加,游客的網絡評論日益增多。針對傳統方法在旅游短文本評論主題分類時出現特征維度過高和數據稀疏等問題,本文提出一種基于卷積神經網絡和SOM的旅游評論主題發現方法。【方法/過程】首先采用詞向量來進行文本表示,降低了特征維度過高問題;其次,通過卷積神經網絡對評論文本提取高階的抽象特征;最后在通過SOM模型基于提取到的抽象特征對主題進行聚類。【結果/結論】實驗結果表明,CNNSOM算法較傳統文本聚類算法在準確率、召回率和F值上都有顯著提高,能夠更好的進行旅游評論的主題發現。

【Purpose/significance】As the number of travel websites increases, the number of online reviews of tourists is increasing. Aiming at the problems of traditional methods such as feature over-dimensioning and data sparseness in the topic classification of short travel text reviews, this paper proposes a topic discovery method for travel reviews based on convolutional neural networks and SOM.【Method/process】Firstly, word vectors are used to represent the texts, which reduces the problem of over-dimensionality. Secondly, high-order abstract features are extracted from the commentary texts by Convolutional Neural Networks. Finally, the topics are clustered based on the abstract features extracted from the SOM model.【Result/conclusion】The experimental results show that compared with the traditional text clustering algorithm, CNN-SOM algorithm has significantly improved the accuracy rate, recall rate and F-value, which can make thematic discovery of travel reviews better.