【摘要】：隨著互聯(lián)網(wǎng)技術(shù)和電子商務(wù)的快速發(fā)展,我們已經(jīng)進(jìn)入了“全民網(wǎng)購”的時(shí)代。消費(fèi)者對商品的在線評論為其他消費(fèi)者、企業(yè)產(chǎn)品反饋提供了重要的資源。因此,如何高效、自動化的剖析在線評論中消費(fèi)者對產(chǎn)品及其相關(guān)特征所持有的態(tài)度成為情感傾向性分析領(lǐng)域的熱點(diǎn)課題。然而,由于中文自然語言本身的多樣性和復(fù)雜性,加之網(wǎng)絡(luò)語言的非規(guī)范性,讓商品在線評論的分析和研究變得更加困難。本文針對目前商品在線評論的情感分析領(lǐng)域中存在的難題,研究了特征級文本情感傾向性分析的理論方法及實(shí)現(xiàn)算法,根據(jù)商品在線評論的文本特點(diǎn),提出了基于句型結(jié)構(gòu)、詞性規(guī)律搭配的在線評論的特征情感三元組提取方法,并根據(jù)特征情感三元組的數(shù)據(jù),用神經(jīng)網(wǎng)絡(luò)算法進(jìn)行商品在線評論情感判別。主要研究工作如下:1.研究了基于特征的文本情感傾向性分析的相關(guān)理論,對篇章級、句子級、實(shí)體特征級三種不同粒度的文本情感分析方法進(jìn)行比較,得出商品在線評論的文本情感分析中實(shí)體特征級的方法能夠提供商品的更詳細(xì)的情感傾向,優(yōu)于其他兩種粒度的情感分析方法。2.提出了一種基于句型結(jié)構(gòu)、詞性規(guī)律搭配的商品在線評論的特征情感三元組提取方法:首先收集領(lǐng)域依賴特征詞集和網(wǎng)絡(luò)流行情感詞集,將網(wǎng)上獲取的評論數(shù)據(jù)經(jīng)過預(yù)處理,得到的主觀子句提取句型模式,根據(jù)詞性規(guī)律并判斷子句的句型模式是否與我們提出的6種在線評論的基本句型模式相同,若相同,則提取對應(yīng)的特征情感三元組;最后,將提取出的特征情感三元組進(jìn)行去噪處理。該方法完美融合了商品在線評論的領(lǐng)域依賴性、程度副詞對情感極性的影響等因素,有效的提取了特征情感三元組,并提高了商品在線評論中產(chǎn)品特征及其相關(guān)情感的識別能力。3.研究了基于人工神經(jīng)網(wǎng)絡(luò)的商品在線評論文本情感傾向性分析。首先建立BP神經(jīng)網(wǎng)絡(luò)及RBF神經(jīng)網(wǎng)絡(luò)模型進(jìn)行訓(xùn)練并仿真;針對神經(jīng)網(wǎng)絡(luò)算法收斂速度慢、易陷入局部最優(yōu)等缺點(diǎn),本文基于全局尋優(yōu)的思想對神經(jīng)網(wǎng)絡(luò)算法進(jìn)行改進(jìn)。改進(jìn)神經(jīng)網(wǎng)絡(luò)算法是將神經(jīng)網(wǎng)絡(luò)模型中的各參數(shù)值進(jìn)行全局優(yōu)化,將隱含層中的權(quán)值整合,利用全局尋優(yōu)的特點(diǎn),確定神經(jīng)網(wǎng)絡(luò)中各參數(shù)最合理的值,從而提高神經(jīng)網(wǎng)絡(luò)算法的性能。分別對改進(jìn)的神經(jīng)網(wǎng)絡(luò)模型進(jìn)行訓(xùn)練并仿真;最后對本文中的四種神經(jīng)網(wǎng)絡(luò)從不同的維度進(jìn)行實(shí)驗(yàn)比較。仿真實(shí)驗(yàn)表明,PSO-BP算法在處理商品在線評論情感傾向性分析問題中有更高的準(zhǔn)確率,但收斂速度更慢;基于PSO-RBF神經(jīng)網(wǎng)絡(luò)在處理情感分析問題時(shí)有較高的正確率,且收斂速度比PSO-BP網(wǎng)絡(luò)更快。
[Abstract]:With the rapid development of Internet technology and e-commerce, we have entered the era of "people-wide online shopping". Online reviews of goods by consumers provide important resources for feedback from other consumers and enterprises. Therefore, how to analyze the consumers' attitude towards the product and its related characteristics efficiently and automatically has become a hot topic in the field of emotional orientation analysis. However, due to the diversity and complexity of Chinese natural language itself, and the non-standardization of network language, it is more difficult to analyze and study the online comment of goods. Aiming at the difficult problems existing in the field of emotional analysis of commodity online comment, this paper studies the theoretical method and realization algorithm of emotional tendency analysis of feature-level text, and puts forward a sentence structure based on the text characteristics of commodity online comment. The feature emotion triple is extracted from the online comment based on the part of speech rule. According to the data of the feature emotion triple, a neural network algorithm is used to judge the online comment emotion of the commodity. The main research work is as follows: 1. This paper studies the related theory of feature-based text affective tendency analysis, and compares three kinds of text emotion analysis methods with different granularity, such as text level, sentence level and entity feature level. It is concluded that the method of entity feature level in the text emotion analysis of commodity online comment can provide the more detailed emotion tendency of commodity, which is better than the other two kinds of granularity emotion analysis method. 2. In this paper, a method of extracting feature emotion triple of online comments based on sentence structure and part of speech law is proposed. Firstly, domain dependent feature word set and network popular emotion word set are collected, and the online comment data are preprocessed. According to the rule of part of speech and judging whether the sentence pattern of the clause is the same as the basic pattern of the six online comments, if the pattern is the same, the corresponding feature emotion triple is extracted. Finally, the extracted feature emotion triples are de-noised. This method combines the domain dependence of commodity online comment, the influence of degree adverbs on emotional polarity and so on, and extracts the characteristic emotional triple effectively. It also improves the recognition ability of product features and related emotions in online reviews of goods. 3. This paper studies the emotional orientation analysis of commodity online comments based on artificial neural network (Ann). Firstly, the BP neural network and RBF neural network model are established for training and simulation. Aiming at the shortcomings of slow convergence rate and easy to fall into local optimization, this paper improves the neural network algorithm based on the idea of global optimization. The improved neural network algorithm is to globally optimize the parameters of the neural network model, integrate the weights in the hidden layer, and make use of the characteristics of the global optimization to determine the most reasonable values of the parameters in the neural network. Thus, the performance of neural network algorithm is improved. The improved neural network model is trained and simulated respectively. Finally, four kinds of neural networks in this paper are compared with each other from different dimensions. The simulation results show that the PSO-BP algorithm has higher accuracy in dealing with the emotional tendency analysis of online comments, but the convergence speed is slower. The PSO-RBF-based neural network has a higher accuracy rate when dealing with emotional analysis problems, and the convergence speed is faster than that of the PSO-BP network.
【學(xué)位授予單位】：上海師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.1

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