本文選題：OTA　切入點：情感分析　出處：《云南財經(jīng)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著經(jīng)濟的發(fā)展,人們對旅游的需求不斷增加,使得在線旅游消費市場呈爆發(fā)式的增長。因此,以攜程網(wǎng)、去哪兒網(wǎng)等為代表的旅游OTA網(wǎng)站積累了海量的用戶的評論文本數(shù)據(jù)。如何從這些大量的文本數(shù)據(jù)中獲取有用的信息以提高用戶體驗成為亟需解決的問題。本文針對OTA網(wǎng)站評論文本數(shù)據(jù)進行了情感分析研究。具體工作如下:首先,本文通過網(wǎng)絡(luò)爬蟲技術(shù)抓取了一定量旅游OTA網(wǎng)站的評論文本數(shù)據(jù)作為研究對象并構(gòu)建了相應(yīng)的分類詞庫以及情感詞庫。由于旅游OTA網(wǎng)站評論文本的特殊性,目前的一些開源的情感詞庫不能十分有效的匹配該類評論文本數(shù)據(jù),而且主流的情感詞庫大部分是二分類的情感判別,無法體現(xiàn)用戶具體的情感傾向程度;也不能將用戶的情感傾向按評價要素細分或者按用戶偏好個性化獲取情感傾向程度�；诖吮疚尼槍υ擃愒u論文本數(shù)據(jù)重新構(gòu)建了專有的按評價要素細分的分類詞庫以及情感詞庫。從而能夠更有效地獲得用戶的情感傾向值。同時也是本文模型構(gòu)建中不可或缺的一部分。其次,本文提出一種基于LSA(潛在語義分析)與DBN(深度信念網(wǎng)絡(luò))的深度學(xué)習(xí)模型。由于傳統(tǒng)的基于文本向量空間所構(gòu)建的文本特征矩陣僅僅體現(xiàn)了文本信息中詞頻的信息,而未能包含詞語于詞語之間潛藏的語義信息(如:一詞多義或一義多詞等)所以在模型擬合過程中其效果往往有所欠缺。因此,本文運用LSA方法將原始的文本特征矩陣進行SVD分解,再通過合理的選取分解后奇異值的個數(shù)重構(gòu)文本特征矩陣。最后,基于重構(gòu)后的文本特征矩陣構(gòu)建了DBN的深度學(xué)習(xí)模型以期通過對文本數(shù)據(jù)的訓(xùn)練學(xué)習(xí)能夠有效地獲得文本的情感傾向值。最后,本文還設(shè)計了六組數(shù)據(jù)與模型的對比實驗以驗證模型的有效性。從最終各個模型十折交叉驗證的總體對比結(jié)果來看,本文構(gòu)建的基于LSA(潛在語義分析)與DBN(深度信念網(wǎng)絡(luò))的深度學(xué)習(xí)模型具有較好的性能。
[Abstract]:With the development of economy, people's demand for tourism is increasing, which makes the online travel consumption market explosive. Therefore, to Ctrip, The travel OTA website, which is represented by the travel website, has accumulated a lot of comments text data from users. How to obtain useful information from these lots of text data in order to improve the user experience has become a problem that needs to be solved in this paper. This paper makes an emotional analysis on the text data of comments on OTA website. The specific work is as follows: first of all, This paper grabs a certain amount of comment text data of tourism OTA website as the research object through web crawler technology, and constructs the corresponding classifying lexicon and emotion lexicon. Because of the particularity of tourism OTA website comment text, At present some open source emotion lexicon can not match this kind of comment text data very effectively and the mainstream emotion lexicon is mostly two-classification emotion judgment which can not reflect the specific emotional tendency of the user. Nor can the emotional tendency of users be subdivided into evaluation elements or individualized by user preferences. Based on this this paper reconstructs a proprietary classification based on evaluation elements for this kind of comment text data. Thesaurus and affective lexicon can obtain the user's emotional tendency value more effectively. It is also an indispensable part of the model building in this paper. Secondly, In this paper, a deep learning model based on LSA (latent semantic Analysis) and DBN (Deep belief Network) is proposed. Because the traditional text feature matrix based on text vector space only embodies the information of word frequency in the text information. However, the semantic information hidden between words (such as polysemy or multi-word) is often lacking in the process of model fitting. In this paper, the original text feature matrix is decomposed by LSA method, then the text feature matrix is reconstructed by selecting the number of singular values reasonably. Based on the reconstructed text feature matrix, a depth learning model of DBN is constructed in order to obtain the emotional tendency of the text effectively by training the text data. In order to verify the validity of the model, six groups of data and model are designed to verify the validity of the model. The depth learning model based on LSA (latent semantic analysis) and DBN (Deep belief Network) has good performance.
【學(xué)位授予單位】：云南財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.1

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