本文選題：中文微博 + 情感分析��； 參考：《鄭州大學(xué)》2014年碩士論文

【摘要】：近年來，情感分析作為自然語言處理中的一個(gè)重要組成部分，一直受到眾多學(xué)者的青睞，其中針對微博的情感分析成為了當(dāng)前研究的熱點(diǎn)。微博作為一種新型交流互動(dòng)方式，賦予了人際交流新的魅力，深受大眾推崇。微博信息看似雜亂無章，其實(shí)具有很重要的應(yīng)用價(jià)值。微博為網(wǎng)友提供了一個(gè)平臺(tái)，網(wǎng)友在這個(gè)平臺(tái)上反映自己在社會(huì)上存在的各種問題，發(fā)布了很多帶有濃烈的個(gè)人情感傾向性和強(qiáng)烈主觀色彩的消息來表達(dá)自己的真實(shí)情感。 本文首先簡述了當(dāng)前文本情感分析領(lǐng)域的相關(guān)研究現(xiàn)狀，簡單介紹了各種情感分類模型，總結(jié)了傳統(tǒng)文本情感分析研究工作，對微博這一新型文本的特點(diǎn)進(jìn)行了相關(guān)介紹和研究。微博情感詞典及相關(guān)資源的構(gòu)建是本文微博情感分析中一個(gè)重要的工作。在微博情感詞典的構(gòu)建中，本文一方面對幾個(gè)比較權(quán)威的開源情感詞典進(jìn)行篩選整理得到基礎(chǔ)情感詞典；另一方面根據(jù)情感詞的句法特點(diǎn)，構(gòu)建句法結(jié)構(gòu)模版，利用模版對情感詞進(jìn)行進(jìn)一步的擴(kuò)展。程度副詞，否定詞和連詞對情感詞有著明顯影響，本文對上述虛詞構(gòu)建了相應(yīng)的詞典。微博中常用表情符號(hào)來明確表達(dá)當(dāng)前情感，本文構(gòu)建了表情符號(hào)情感詞典。并將帶有情感色彩的網(wǎng)絡(luò)用語進(jìn)行抽取成網(wǎng)絡(luò)用詞情感詞典。同時(shí)針對多義性的情感詞和隱含性的情感句構(gòu)建了一些規(guī)則。整合基礎(chǔ)情感詞典，擴(kuò)展情感詞典，，表情符號(hào)情感詞典，網(wǎng)絡(luò)用詞情感詞典，最終得到本文的微博情感詞典。 本文利用最終構(gòu)建的微博情感詞典對于微博文本進(jìn)行情感分析。為了檢驗(yàn)本文構(gòu)建的微博情感詞典和規(guī)則對于微博情感分析的有效性，本文選用了基于最大熵和基于支持向量機(jī)兩種分類模型作為對比方法；為了驗(yàn)證詞典的適用性，本文選取了兩種的實(shí)驗(yàn)語料，一種是各種分類是均勻分布的平衡語料,另一種是各種分類是隨機(jī)分布的非平衡語料。實(shí)驗(yàn)對比結(jié)果中，可以看到在兩種微博語料中，利用本文構(gòu)建的微博情感詞典和規(guī)則對于微博情感分析的效果比另外兩種分類模型的效果要好，驗(yàn)證了本文構(gòu)建的微博情感詞典對于微博情感分析的有效性和適用性。
[Abstract]:In recent years, affective analysis, as an important part of natural language processing, has been favored by many scholars, among which emotional analysis for Weibo has become a hot research topic. As a new way of communication and interaction, Weibo endows new charm of interpersonal communication and is highly praised by the public. Weibo information seems to be messy, in fact, has very important application value. Weibo provides a platform for netizens to reflect their problems in society and release a lot of messages with strong personal feelings and strong subjective color to express their true feelings. Firstly, this paper briefly introduces the current research situation in the field of text emotion analysis, introduces a variety of emotion classification models, and summarizes the traditional text emotion analysis research work. This paper introduces and studies the characteristics of Weibo, a new text. The construction of Weibo affective dictionary and related resources is an important work in this paper Weibo affective analysis. In the construction of the Weibo emotion dictionary, on the one hand, this paper sorts out several authoritative open source emotion dictionaries to get the basic emotion dictionary; on the other hand, according to the syntactic characteristics of the affective words, it constructs the syntactic structure template. Use template to extend affective words further. Degree adverbs, negative words and conjunctions have obvious influence on affective words. Emoji is commonly used in Weibo to express the current emotion clearly. In this paper, the emoji emotion dictionary is constructed. And the network words with emotional color are extracted into the network words emotion dictionary. At the same time, some rules are constructed for polysemous affective words and implicit affective sentences. The basic emotion dictionary, the extended emotion dictionary, the emoticons emotion dictionary, the network words emotion dictionary are integrated. Finally, the Weibo emotion dictionary of this paper is obtained. This paper uses the final Weibo emotion dictionary to analyze the emotion of Weibo text. In order to test the validity of the Weibo emotion dictionary and rules for Weibo affective analysis, two classification models based on maximum entropy and support vector machine are selected as comparison methods, and the applicability of the dictionary is verified. In this paper, two kinds of experimental corpus are selected, one is that each classification is a balanced corpus with uniform distribution, the other is that each classification is a non-equilibrium corpus with random distribution. By comparing the results of the experiment, we can see that in the two kinds of Weibo corpus, the effect of using the Weibo emotion dictionary and rules constructed in this paper on Weibo affective analysis is better than that of the other two classification models. The validity and applicability of the Weibo emotion dictionary for Weibo affective analysis are verified.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.092

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 張成功;劉培玉;朱振方;方明;;一種基于極性詞典的情感分析方法[J];山東大學(xué)學(xué)報(bào)(理學(xué)版);2012年03期

本文編號(hào)：1972016