本文選題：情感分析 + 情感詞表��； 參考：《南開(kāi)大學(xué)》2014年博士論文

【摘要】：微博等在線社交媒體在輿論的傳播方面所起的作用已越來(lái)越明顯,社交媒體中的用戶可以自由發(fā)表他們對(duì)某一事件的觀點(diǎn)和看法,也可以通過(guò)文字、圖片、視頻等形式發(fā)泄情緒。由于社交媒體中的用戶處于不同的社交網(wǎng)絡(luò)中,信息的傳播非常迅速,所以在特定事件情境下,社交媒體用戶極易產(chǎn)生群體極化現(xiàn)象,甚至導(dǎo)致網(wǎng)絡(luò)或?qū)嶋H生活中的群體性事件。用戶表達(dá)的情感不僅能影響事件的傳播速度,而且情感能夠相互感染,不利情感或負(fù)面情緒能夠激發(fā)用戶的負(fù)面行為,促使事件朝著不利的方向發(fā)展。所以有必要對(duì)微博等社交媒體用戶在特定事件情境下的情感進(jìn)行分析,判斷用戶的情感類(lèi)型和情感極性強(qiáng)度,尋找影響用戶情感表達(dá)和傳播的影響因素,進(jìn)行有針對(duì)性地監(jiān)控和引導(dǎo)。 基于以上背景,本文將研究問(wèn)題集中于特定事件情境下中文微博用戶的情感挖掘與情感傳播研究。本研究需要解決三大研究問(wèn)題,分別為情感特征識(shí)別問(wèn)題、情感特征統(tǒng)計(jì)和描述問(wèn)題和情感傳播問(wèn)題。主要研究工作包括：第一,構(gòu)建中文情感分類(lèi)詞表,包括情緒分類(lèi)詞表和評(píng)價(jià)分類(lèi)詞表。詞表中的情感詞一方面來(lái)源于現(xiàn)有三個(gè)中英文情感詞表,另一方面來(lái)源于待分析事件的微博文本語(yǔ)料,采用基于HowNet知識(shí)庫(kù)標(biāo)注的方法實(shí)現(xiàn)情感詞的分類(lèi)和極性強(qiáng)度判斷。最終情緒分類(lèi)詞表包含12個(gè)大類(lèi)和32個(gè)小類(lèi),共3773個(gè)情緒詞；評(píng)價(jià)分類(lèi)詞表包含8個(gè)大類(lèi)和100個(gè)小類(lèi),共12844個(gè)評(píng)價(jià)詞；第二,實(shí)現(xiàn)情感詞可視化和情感特征分類(lèi)統(tǒng)計(jì)。通過(guò)情感詞在同一微博中的共現(xiàn)計(jì)算情感詞之間的關(guān)系,然后通過(guò)位置算法將情感詞之間的關(guān)系通過(guò)圖形的方式進(jìn)行展示,同時(shí)還通過(guò)字體的大小和顏色來(lái)表示情感詞的熱度和極性強(qiáng)度。研究發(fā)現(xiàn)高頻中心詞反映了事件的主導(dǎo)情感類(lèi)型,越靠近邊緣位置,情感詞越能反映普通公眾的情感。對(duì)情緒詞的分類(lèi)統(tǒng)計(jì)能發(fā)現(xiàn)特定事件下用戶所表達(dá)的各情緒類(lèi)型的強(qiáng)度。將表情符號(hào)按正負(fù)面極性分布進(jìn)行時(shí)間序列的統(tǒng)計(jì)可以發(fā)現(xiàn)一些難以發(fā)現(xiàn)的水軍廣播,去除水軍廣播后正負(fù)面表情強(qiáng)度的變化趨勢(shì)相似；第三,構(gòu)建特定事件信息傳播網(wǎng)絡(luò),通過(guò)社會(huì)網(wǎng)絡(luò)分析方法分析事件信息傳播網(wǎng)絡(luò)中的關(guān)鍵用戶、信息傳播距離、傳播網(wǎng)絡(luò)集聚程度。將用戶情緒嵌入事件傳播網(wǎng)絡(luò)中,進(jìn)行信息傳播網(wǎng)絡(luò)用戶情感可視化,了解用戶情感的分布情況。分析用戶的情感表達(dá)與用戶的角色之間的關(guān)系,發(fā)現(xiàn)決策者應(yīng)關(guān)注表達(dá)“激動(dòng)”、“詆毀”、“同意”或“反對(duì)”等情緒的用戶,表達(dá)這些情緒的用戶更容易在信息傳播中起關(guān)鍵作用。 本研究的創(chuàng)新點(diǎn)主要有三個(gè)方面：第一,完善了情感詞表的構(gòu)建。目前雖已有少數(shù)關(guān)于中文情感詞典構(gòu)建的相關(guān)研究,但一方面這些情感詞表無(wú)法公開(kāi)使用,少數(shù)可用的情感詞表僅僅將情感詞分為正面和負(fù)面兩類(lèi),由于這方面的限制,目前的情感分析大多集中于對(duì)句子或文本的正負(fù)面極性進(jìn)行判斷,無(wú)法獲知文本中具體的情感類(lèi)型和情感強(qiáng)度。本研究將整個(gè)情感詞表分為情緒分類(lèi)詞表和評(píng)價(jià)分類(lèi)詞表,不僅能實(shí)現(xiàn)極性的計(jì)算,而且能夠?qū)崿F(xiàn)具體情緒類(lèi)型的分析。第二,將可視化技術(shù)應(yīng)用于情感描述,有助于情感分析方法的完善。目前自然語(yǔ)言可視化較常見(jiàn)的是對(duì)文本標(biāo)簽或關(guān)鍵詞進(jìn)行標(biāo)簽云的展示,主要是對(duì)文本主題的可視化,而情感詞可視化的研究和應(yīng)用并不多見(jiàn)。本研究不僅僅將情感詞進(jìn)行可視化表示,還將情感詞之間的關(guān)系和情感詞的極性強(qiáng)度特征通過(guò)圖形進(jìn)行表達(dá)。另外,本研究不僅對(duì)情感詞進(jìn)行了可視化,還介紹了將用戶情感在事件信息傳播網(wǎng)絡(luò)中進(jìn)行可視化的方法,通過(guò)多種可視化技術(shù)和算法可為決策者提供更直觀的用戶情感信息。第三,推進(jìn)了特定事件下中文社交網(wǎng)絡(luò)情感傳播研究。目前已有相關(guān)英文情感傳播的研究,但這些研究多數(shù)關(guān)注用戶日常交流網(wǎng)絡(luò)的情感互動(dòng),而用戶對(duì)事件信息的情感及這種情感如何在事件傳播網(wǎng)絡(luò)中進(jìn)行傳播和分布的研究較少,為了探索情感傳播相關(guān)因素,本研究還對(duì)用戶情感與用戶在事件傳播中扮演的角色之間的關(guān)系進(jìn)行了分析。 在理論研究方面,本文基于心理學(xué)的研究和HowNet本體構(gòu)建了情感詞的分類(lèi)體系,可供后續(xù)研究作參考。在方法方面,提供了情感知識(shí)的表示方法,有助于目前情感分析方法的完善,并結(jié)合社會(huì)網(wǎng)絡(luò)分析方法和相關(guān)性分析方法進(jìn)行用戶情感傳播研究,有助于情感傳播研究方法的完善。在實(shí)踐方面,有助于政府有關(guān)部門(mén)了解公眾在事件發(fā)生過(guò)程中的情感傳播狀況,為避免公眾情感的集聚和極化,提供有針對(duì)性的信息。有助于企業(yè)或個(gè)人了解微博公眾對(duì)事件的情緒反應(yīng)和評(píng)價(jià),通過(guò)公眾情感擴(kuò)散規(guī)律制定有針對(duì)性的應(yīng)對(duì)策略。有助于公共管理部門(mén)、企業(yè)了解公眾對(duì)自身服務(wù)或產(chǎn)品的情緒和評(píng)價(jià),以改進(jìn)自身服務(wù)或產(chǎn)品。
[Abstract]:Online social media, such as micro-blog, has been playing a more and more important role in the dissemination of public opinion. Users in social media can freely express their views and views on an event. They can also vent their emotions through words, pictures, video and other forms. Because users in social media are in different social networks, information is transmitted. Sowing is very fast, so in particular event situations, social media users are very easy to generate group polarization, and even lead to group events in the network or in real life. The emotions expressed by users can not only affect the speed of the event, but also affect each other, and the negative emotion or negative emotion can stimulate the negative behavior of the user. In order to promote the development of events in a disadvantageous direction, it is necessary to analyze the emotions of social media users such as micro-blog and other social media in particular event situations, to determine the emotional type and emotional polarity of the users, to find the influencing factors that affect the expression and dissemination of the users' emotions, and to carry out a targeted monitoring and guidance.
Based on the above background, this study focuses on the research of emotional mining and emotional communication of Chinese micro-blog users under specific event situations. This study needs to solve three major research problems, such as emotional feature identification, emotional feature statistics and description and emotional communication. The main research work includes: first, construction The emotional classification word list, including the emotion classification word list and the evaluation classification word list. The emotional words in the word list are derived from three Chinese and English emotional words, on the other hand, from the micro-blog text data of the events to be analyzed, using the method based on HowNet knowledge base to realize the classification of emotional words and the judgment of polarity. The classification word list consists of 12 large classes and 32 small classes, with a total of 3773 emotional words. The evaluation classification word list contains 8 large classes and 100 small classes, with a total of 12844 evaluation words; second, realizes the visualization of emotional words and the classification statistics of emotional features. The relationship between the emotion words in the same micro-blog is calculated by the emotion words, and then the location algorithm will be used. The relationship between emotional words is displayed in a graphic way, and the heat and polarity of emotion words are expressed by the size and color of the font. The study finds that the high frequency center words reflect the dominant emotion type of the event, the closer to the edge position, the more emotional words can reflect the emotions of the general public. The intensity of the emotional types expressed by the user under the specific event is found. The statistics of the emoticons according to the positive and negative polarity distribution of the time series can find some hard to find water army broadcasting, and the change tendency of the negative expression intensity is similar after the water army broadcasting. Third, the network of information dissemination for specific events is constructed and the society is constructed through society. The network analysis method analyzes the key users in the event information communication network, the distance of information propagation and the degree of network aggregation. The user emotion is embedded in the event propagation network, and the user emotion visualization is carried out, the distribution of users' emotion is understood. The relationship between the user's emotional expression and the user's role is analyzed. Decision makers should pay attention to users expressing "excitement", "denigrating", "consent" or "objection", and the users who express these emotions are more likely to play a key role in the dissemination of information.
There are three main innovative points in this study. First, the construction of emotional vocabulary is perfected. Although there are a few related studies on the construction of Chinese affective lexicon, on the one hand, these emotional words can not be used publicly. A few of the emotional words can only be divided into positive and negative two categories, because of this limitation, Most of the current emotional analysis is focused on the judgment of the positive and negative polarity of the sentence or text. It is impossible to know the specific emotional type and emotional intensity in the text. This study divides the whole emotional word list into the emotion classification word list and the evaluation classification word list, not only can realize the calculation of the polarity, but also can realize the analysis of the specific emotional type. Two, the application of visual technology to emotional description can help to improve the method of emotional analysis. At present, the more common natural language visualization is to display the label cloud of text labels or keywords. It is mainly the visualization of the text theme, but the research and application of the emotion word visualization is not much. In addition, this study not only visualizations of emotional words, but also introduces the method of visualizing user emotion in event information communication network, and through a variety of visualization techniques and algorithms for decision-makers. More intuitive user emotional information. Third, promote the study of emotional communication of Chinese social networks under specific events. There is a study of emotional communication in English, but most of these studies are concerned with the emotional interaction of the user's daily communication network, and how the user's feelings about the event information and this emotion are transmitted in the event communication network. There are few studies on sowing and distribution. In order to explore the related factors of emotional communication, this study also analyzes the relationship between the user's emotion and the role played by the user in the event communication.
In the field of theoretical research, this paper constructs the classification system of emotional words based on the research of psychology and the HowNet ontology, which can be used as a reference for subsequent research. In the way, it provides the expression of emotional knowledge, which is helpful to the improvement of the current emotional analysis methods, and combines the social network analysis method and the correlation analysis method to carry on the user's feeling. The study of sense transmission helps to improve the research methods of emotional communication. In practice, it helps the government departments to understand the public's emotional transmission in the event of the event, and to provide pertinent information to avoid the gathering and polarization of public sentiment. It will help enterprises or individuals understand the emotional response of the micro-blog public to the event and Evaluation, the formulation of targeted coping strategies through the law of public emotional diffusion. It helps the public management department to understand the public's feelings and evaluations of their own services or products to improve their own services or products.
【學(xué)位授予單位】：南開(kāi)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：G206

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 葉璐;;微博中的負(fù)面情緒傳播分析[J];今傳媒;2012年02期

2 秦毅;;漢語(yǔ)條件句的構(gòu)成類(lèi)型及其英譯[J];重慶科技學(xué)院學(xué)報(bào)(社會(huì)科學(xué)版);2009年05期

3 楊久華;;試論網(wǎng)絡(luò)群體性事件發(fā)生的模式、原因及其防范[J];重慶社會(huì)主義學(xué)院學(xué)報(bào);2009年04期

4 金云波;許遠(yuǎn)理;;網(wǎng)絡(luò)異化對(duì)網(wǎng)絡(luò)情緒傳播的影響[J];重慶文理學(xué)院學(xué)報(bào)(社會(huì)科學(xué)版);2011年02期

5 江敏;肖詩(shī)斌;王弘蔚;施水才;;一種改進(jìn)的基于《知網(wǎng)》的詞語(yǔ)語(yǔ)義相似度計(jì)算[J];中文信息學(xué)報(bào);2008年05期

6 王振宇;吳澤衡;胡方濤;;基于HowNet和PMI的詞語(yǔ)情感極性計(jì)算[J];計(jì)算機(jī)工程;2012年15期

7 陽(yáng)愛(ài)民;林江豪;周詠梅;;中文文本情感詞典構(gòu)建方法[J];計(jì)算機(jī)科學(xué)與探索;2013年11期

8 胡辰;李丹超;;淺談微博對(duì)傳統(tǒng)媒體議程設(shè)置的影響[J];新聞世界;2010年09期

9 宋紹成,畢強(qiáng),楊達(dá);信息可視化的基本過(guò)程與主要研究領(lǐng)域[J];情報(bào)科學(xué);2004年01期

10 曾潤(rùn)喜;徐曉林;;網(wǎng)絡(luò)輿情對(duì)群體性突發(fā)事件的影響與作用[J];情報(bào)雜志;2010年12期

相關(guān)博士學(xué)位論文 前1條

1 董光恒;積極情緒與消極情緒啟動(dòng)對(duì)沖動(dòng)控制影響的差異研究[D];遼寧師范大學(xué);2008年

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