發(fā)布時(shí)間：2018-04-05 08:39

  本文選題：微博　切入點(diǎn)：情感分類(lèi)　出處：《浙江師范大學(xué)》2014年碩士論文

【摘要】：近年來(lái),隨著中國(guó)經(jīng)濟(jì)的快速發(fā)展,中國(guó)的股票市場(chǎng)發(fā)展也呈現(xiàn)迅猛之勢(shì)。中國(guó)股市已擁有2467家上市公司,滬深股市總市值23.5萬(wàn)億,股民數(shù)量已達(dá)到1.6億,中國(guó)股市已經(jīng)成為全球市值的第三大市場(chǎng)。對(duì)股民而言,互聯(lián)網(wǎng)財(cái)經(jīng)類(lèi)消息與他們的利益息息相關(guān)。 微博作為一種新型的社交工具,由于其簡(jiǎn)短寫(xiě)作,便捷發(fā)布,實(shí)時(shí)交互的特點(diǎn)深受大眾歡迎,微博已成為國(guó)內(nèi)第二大網(wǎng)絡(luò)社交媒介,也是第二大輿情源頭。面向財(cái)經(jīng)類(lèi)的微博信息分析,關(guān)注公眾對(duì)財(cái)經(jīng)市場(chǎng)的反應(yīng)——情感,可以為市場(chǎng)預(yù)測(cè)提供參考,為財(cái)經(jīng)行業(yè)從業(yè)人員和投資者服務(wù)。因此,以財(cái)經(jīng)領(lǐng)域作為研究實(shí)例,分析微博輿情有現(xiàn)實(shí)意義和應(yīng)用價(jià)值。 在針對(duì)財(cái)經(jīng)微博的情感態(tài)度分析研究中,構(gòu)建了一個(gè)完整的分類(lèi)模型,主要從規(guī)范化、分類(lèi)、命名實(shí)體識(shí)別、情感分析、趨勢(shì)預(yù)測(cè)等方面開(kāi)展研究。但是本文將重心放在情感分析上,情感傾向分類(lèi)也被稱(chēng)為觀點(diǎn)挖掘(Opinion Mining)或者情感極性分類(lèi),可以理解為用戶(hù)對(duì)某客體表達(dá)自身觀點(diǎn)所持的態(tài)度是支持、反對(duì)、中立,也就是常說(shuō)的正面情感、負(fù)面情感、中性情感。在論文的具體實(shí)施過(guò)程中,研究的主要內(nèi)容包括以下幾部分： (1)研究了公司組織機(jī)構(gòu)名稱(chēng)全稱(chēng)及簡(jiǎn)稱(chēng)的語(yǔ)法構(gòu)成、語(yǔ)義特點(diǎn)及組織規(guī)律,并結(jié)合金融領(lǐng)域特有的情感詞,使用情感傾向點(diǎn)互信息算法(SO-PMI)構(gòu)建了金融領(lǐng)域詞典。 (2)分析研究中文微博的特點(diǎn),在結(jié)合網(wǎng)絡(luò)語(yǔ)言及金融語(yǔ)言特點(diǎn)的基礎(chǔ)上,構(gòu)建了網(wǎng)絡(luò)用語(yǔ)詞典和否定詞、程度副詞及表情符詞典,對(duì)深入研究情感態(tài)度挖掘具有重要幫助。 (3)提出了情感加權(quán)計(jì)算方法,將構(gòu)建的各類(lèi)詞典應(yīng)用到情感分類(lèi)之中,實(shí)現(xiàn)情感分類(lèi)值的量化計(jì)算。 最后通過(guò)新浪API獲取一段時(shí)間內(nèi)含有公司名稱(chēng)的財(cái)經(jīng)微博,在經(jīng)過(guò)預(yù)處理、分詞和特征選擇之后,用詞典的情感分類(lèi)方法對(duì)其進(jìn)行分類(lèi)。實(shí)驗(yàn)驗(yàn)證了金融領(lǐng)域詞典、網(wǎng)絡(luò)詞典、和表情詞典的重要性,并將各種詞典都完備下的實(shí)驗(yàn)數(shù)據(jù)和實(shí)際股市走向進(jìn)行對(duì)比,說(shuō)明實(shí)驗(yàn)數(shù)據(jù)在實(shí)際生活中具有現(xiàn)實(shí)意義,通過(guò)進(jìn)一步研究可運(yùn)用于股票投資。
[Abstract]:In recent years, with the rapid development of China's economy, China's stock market is also showing a rapid trend.China's stock market has 2467 listed companies, Shanghai and Shenzhen stock market market value 23.5 trillion, the number of shareholders has reached 160 million, the Chinese stock market has become the world's third-largest market market value.For investors, Internet financial news and their interests are closely linked.Weibo as a new type of social tool, because of its short writing, convenient release, real-time interaction characteristics of popular welcome, Weibo has become the second largest social media in China, but also the second source of public opinion.Weibo's information analysis, focusing on the public's reaction to the financial market, can provide a reference for market forecasting and serve as a service for practitioners and investors in finance and economics.Therefore, take the finance and economics domain as the research example, analysis Weibo public opinion has the realistic significance and the application value.In the study of financial Weibo's affective attitude analysis, a complete classification model is constructed, mainly from standardization, classification, named entity identification, emotional analysis, trend prediction and so on.However, this paper focuses on emotional analysis, which is also called opinion mining or emotional polarity classification, which can be understood as support, opposition and neutrality of the user's attitude towards an object expressing its own views.That is to say, positive emotion, negative emotion, neutral emotion.In the specific implementation of the paper, the main content of the study includes the following parts:(1) this paper studies the grammatical structure, semantic characteristics and organization rules of the full name and abbreviation of company organization, and constructs the financial domain dictionary by using the affective point mutual information algorithm (SO-PMI), which is a special affective word in the financial field.2) analyzing and studying the characteristics of Chinese Weibo, on the basis of combining the characteristics of network language and financial language, this paper constructs a dictionary of network terms, negative words, adverbs of degree and emoji, which is of great help to the further study of emotional attitude mining.(3) an affective weighted calculation method is put forward, and the constructed dictionaries are applied to emotional classification to realize the quantification calculation of emotional classification value.Finally, the financial and economic Weibo with company name was obtained by Sina API for a period of time. After preprocessing, participle and feature selection, it was classified by the emotion classification method of dictionary.The experiment verifies the importance of financial field dictionary, network dictionary and expression dictionary, and compares the experimental data with the trend of real stock market, which shows that the experimental data have practical significance in real life.It can be applied to stock investment through further research.
【學(xué)位授予單位】：浙江師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP391.1;TP393.092

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