本文選題：教材在線評(píng)論　切入點(diǎn)：細(xì)粒度情感分析　出處：《新疆師范大學(xué)》2017年碩士論文

【摘要】：隨著電子商務(wù)的迅猛發(fā)展,網(wǎng)上書店已經(jīng)成為很多商家銷售圖書的重要平臺(tái),網(wǎng)絡(luò)購物因其價(jià)格實(shí)惠、購買便利等優(yōu)勢(shì),逐漸成為人們購買圖書的首選方式。越來越多的用戶閱讀圖書后,也熱衷于在線分享自己對(duì)所購圖書的真實(shí)看法或體驗(yàn)。電商網(wǎng)站中涌現(xiàn)的大量圖書評(píng)論,蘊(yùn)含著用戶對(duì)圖書的評(píng)價(jià),潛在消費(fèi)者可以據(jù)此降低購買風(fēng)險(xiǎn),從而獲得滿意的購物結(jié)果,商家和出版社也能根據(jù)其做出合理有效的決策�？梢妶D書在線評(píng)論的挖掘,對(duì)消費(fèi)者、商家和出版社有很重要的意義和實(shí)用價(jià)值。本文運(yùn)用細(xì)粒度情感分析技術(shù),分析教材類圖書的在線評(píng)論,挖掘教材特征級(jí)的情感傾向性分析結(jié)果,為消費(fèi)者和商家提供有價(jià)值的參考信息。本文首先分析了粗顆粒度和細(xì)顆粒度在線評(píng)論情感傾向性分析的國(guó)內(nèi)外研究現(xiàn)狀,其次詳細(xì)調(diào)研了細(xì)粒度情感分析的相關(guān)理論和技術(shù),明確了情感分析的步驟和每步中的關(guān)鍵技術(shù)。在此基礎(chǔ)上,通過網(wǎng)頁爬蟲軟件采集教材的在線評(píng)論信息,對(duì)采集數(shù)據(jù)進(jìn)行去重、清洗、拼音英語替換等去噪處理,形成教材評(píng)論分析的訓(xùn)練和測(cè)試語料。利用中文分詞軟件和自定義分詞詞典,完成并優(yōu)化評(píng)論語料的分詞和詞性標(biāo)注。然后,基于標(biāo)注結(jié)果,根據(jù)產(chǎn)品特征通常為名詞和名詞性短語的規(guī)律,歸納了名詞性短語的構(gòu)詞規(guī)則,利用該規(guī)則從訓(xùn)練語料中抽取候選產(chǎn)品特征,通過詞頻過濾和人工校驗(yàn)進(jìn)行篩選,建成教材產(chǎn)品特征詞庫。接著,根據(jù)教材評(píng)論的領(lǐng)域特性,在通用情感詞典的基礎(chǔ)上,利用訓(xùn)練語料構(gòu)建了領(lǐng)域情感詞典、網(wǎng)絡(luò)情感詞典和極性修飾情感詞典,形成面向教材評(píng)論的情感詞典資源。最后,分析了現(xiàn)有SBV算法運(yùn)用于教材評(píng)論時(shí)還無法識(shí)別某些特征-意見對(duì)的問題,提出改進(jìn)思路,利用本文構(gòu)建的極性詞典和特征詞庫,設(shè)計(jì)教材評(píng)論文本的情感傾向性分析算法。通過測(cè)試語料進(jìn)行實(shí)驗(yàn),分析結(jié)果表明,本文算法和詞典資源相比通用情感詞典和SBV算法,評(píng)價(jià)指標(biāo)明顯提升,從而證明了本文構(gòu)建資源和算法設(shè)計(jì)的有效性。
[Abstract]:With the rapid development of electronic commerce, online bookstore has become an important platform for many merchants to sell books. Because of its advantages of affordable price and convenient purchase, online shopping has gradually become the first choice for people to buy books.More and more users are keen to share their true views and experiences of the books they buy.A large number of book reviews emerge in e-commerce websites, which contain the evaluation of books by users. The potential consumers can reduce the purchase risk and obtain satisfactory shopping results. The merchants and publishers can also make reasonable and effective decisions according to them.It can be seen that the mining of online reviews of books is of great significance and practical value to consumers, merchants and publishers.In this paper, the fine-grained emotion analysis technology is used to analyze the online reviews of textbook books, and to excavate the result of affective tendency analysis at the characteristic level of textbooks, which provides valuable reference information for consumers and merchants.In this paper, firstly, the current situation of the research on coarse-grained and fine-grained online reviews of affective tendency analysis is analyzed, and then the relevant theories and techniques of fine-grained emotional analysis are investigated in detail.The process of affective analysis and the key techniques in each step are defined.On this basis, the online comment information of the textbook is collected by the web crawler software, and the data is removed, cleaned and replaced by the Pinyin English to form the training and testing corpus for the review analysis of the textbook.The Chinese word segmentation software and the custom word segmentation dictionary are used to complete and optimize the word segmentation and part of speech tagging of the comment corpus.Then, based on the tagging results, according to the rule that product features are usually nouns and noun phrases, the word-formation rules of nominal phrases are summarized, and the candidate product features are extracted from the training corpus by using this rule.Through word frequency filtering and manual check to screen, build the textbook product feature lexicon.Then, according to the domain characteristics of textbook review and on the basis of the general emotion dictionary, the domain emotion dictionary, the network emotion dictionary and the polarity modified emotion dictionary are constructed by using the training corpus to form the emotion dictionary resources for the textbook review.Finally, this paper analyzes the problem that the existing SBV algorithm can not recognize some character-opinion pairs when it is used in the textbook review, and puts forward some improved ideas, and makes use of the polarity dictionary and feature lexicon constructed in this paper.Design an algorithm for analyzing the emotional orientation of the review text of the textbook.The experimental results show that compared with the general emotion dictionary and SBV algorithm, the evaluation index of this algorithm is significantly improved, which proves the validity of the resource and algorithm design in this paper.
【學(xué)位授予單位】：新疆師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.1;G423.3

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本文編號(hào)：1691681