Stock Market Prediction Using Heat of Related Keywords on Micro Blog

Whether the stock market investors’ emotion can influence the stock market itself is one of the hot topic in financial research. In this paper, a method based on the heat of related keywords on Micro Blog is proposed, as Micro Blog is an ideal source for capturing public opinions towards certain topic. We choose Shanghai Composite index as the research object, through correlation analysis, Granger causality analysis, and support vector machine classification, the results have shown that the keywords heat on micro blog can make a short-time prediction of stock market, and the keyword which expresses negative emotion have more powerful prediction ability.

微博客(Micro-blog)在图书馆中的应用研究

文章首先介绍了微博客(Micro-blog)的特性,阐述了微博客在图书馆中应用的意义、可行性、应用机制以及应用领域,并指出了微博客在应用过程中需要注意的问题。

Micro Blog, Macro Power

New Internet activity is becoming a channel for delivering information Cai Qi found himself unprecedentedly popular in his Internet career just one day after he announced the

On the Linguistic Features of Comments in Micro-blogs from Different Social Status

The study makes a contrastive analysis of the differences in the comments of people from different social status in their Mini-blogs.It aims to find the manifestations of those differences by analyzing the underlying causes and provide constructive suggestions on how to build a more harmonious public platform,which is of its great significance.

Chinese micro-blog sentiment classification through a novel hybrid learning model

With the rising and spreading of micro-blog, the sentiment classification of short texts has become a research hotspot. Some methods have been developed in the past decade. However, since the Chinese and English are different in language syntax, semantics and pragmatics, sentiment classification methods that are effective for English twitter may fail on Chinese micro-blog. In addition, the colloquialism and conciseness of short Chinese texts introduces additional challenges to sentiment classification. In this work, a novel hybrid learning model was proposed for sentiment classification of Chinese micro-blogs, which included two stages. In the first stage, emotional scores were calculated over the whole dataset by utilizing an improved Chinese-oriented sentiment dictionary classification method. Data with extremely high or low scores were directly labeled. In the second stage, the remaining data were labeled by using an integrated classification method based on sentiment dictionary, support vector machine(SVM) and k-nearest neighbor(KNN). An improved feature selection method was adopted to enhance the discriminative power of the selected features. The two-stage hybrid framework made the proposed method effective for sentiment classification of Chinese micro-blogs. Experiments on the COAE2014(Chinese Opinion Analysis Evaluation 2014) dataset show that the proposed method outperforms other schemes.

The Simulation of Rumor’S Spreading and Controlling in Micro-Blog Users’ Network

In this work, a rumor’s spreading and controlling in a directed Micro-blog user network being consisted with 580 000 nodes are simulated. By defining some authority nodes that release anti-rumor information as the prevention strategy, the effect of the nodes’ role in network on rumor’s suppression is studied. The findings show that rumor will be spread out fast and reach a stable level within limited steps. The suppression of rumor is more predominated by the intervening opportunity, the earlier the intervention strategy was implemented, the better the rumor’s controlling could be achieved. The controlling effect is less relevant with the role of the authority nodes in network.

A Study on the Evolution of Mechanism of Infectious Disease Analysis Model of Event Using Micro-Blog Platform

After the occurrence of unexpected group events of network, the relevant opinion information will spread rapidly through micro-blog, and the negative public opinion information will aggravate the unexpected the group events to upgrade and expand the scope of harm. It is difficult to deal. So public opinion control is very important. In this paper, we establish an influence model for spreading of public opinion based on SIR model. Through the political analysis, this paper finds that the network group events will subside, but the influence scope, time and ability of event cannot be ignored. As a result of this study, the corresponding strategies are put forward in this paper.

Impact of user influence on information multi-step communication in a micro-blog

User influence is generally considered as one of the most critical factors that affect information cascading spreading. Based on this common assumption, this paper proposes a theoretical model to examine user influence on the information multi-step communication in a micro-biog. The multi-steps of information communication are divided into first-step and non-first-step, and user influence is classified into five dimensions. Actual data from the Sina micro-blog is collected to construct the model by means of an approach based on structural equations that uses the Partial Least Squares （PLS） technique. Our experimental results indicate that the dimensions of the number of fans and their authority significantly impact the information of first-step conxrnunication. Leader rank has a positive impact on both first-step and non-first-step communication. Moreover, global centrality and weight of friends are positively related to the information non-first-step communication, but authority is found to have much less relation to it.

Evaluation on the Influence of Different Meteorological Government Micro-blog

Taking Meteorological Knowledge official micro-blog ＂ Meteorological Knowledge＂ and Beijing Meteorological Bureau official micro-blog ＂ Meteorology Beijing＂ as the research objects,the differences of different types of meteorological government micro-blog and the contribution of each factor were analyzed by comparing changes of the number of fans,micro-blog comments and forwarding and other key elements during March1 to May 31 in 2012. The results showed that the number of fans was one of important indexes to determine the influence of meteorological government micro-blog,and the high active fans played a more prominent role. During the period of study,the fans number of ＂ Meteorology Beijing＂ was far more than ＂ Meteorological Knowledge＂,the daily micro-blog released number and comment forwarding number were both more than ＂ Meteorological knowledge＂,but the proportion of active fans of ＂ Meteorological knowledge＂ was larger than ＂ Meteorology Beijing＂. Timeliness was of greater contribution to advancing the meteorological government micro-blog influence. During the period of study,the proportions of comments and forwarding number of morning weather forecast were the largest,the evening weather forecast took the second place,and the noon was the smallest.But the influence of noon weather forecast micro-blog increased most highly approximately by 15% because of the higher timeliness. The content and form also made great contribution to the influence of meteorological government micro-blog. Comparison of different types of meteorological micro-blog showed that,the number of fans and the influence of meteorological government micro-blog which mainly published real-time meteorological information were larger than which mainly published meteorological popular science knowledge.

基于铀媒指数的省级档案馆微博信息服务优化研究

基于铀媒指数对8个省级档案馆微博的运营数据进行分析,探索省级档案馆微博信息服务的发展现状,发现其在重视程度、更新速度、内容特色、互动意识方面存在问题,进而提出省级档案馆微博信息服务的优化提升路径,以期为档案馆信息服务提供全新视角,为后续档案馆微博平台的应用研究提供参考。

微博短视频属性对旅游目的地形象的影响

移动互联网的发展推动新媒体技术的提升,越来越多的旅游目的地开始重视运用新媒体特别是短视频营销的方式进行旅游目的地形象构建、塑造及传播。以微博短视频属性的具体要素为出发点,基于自我一致性理论与旅游地形象理论,通过自然实验方法分析微博账号类型和博主类型的短视频对旅游目的地形象及游客旅游意向的影响。研究发现,微博账号类型对目的地形象的直接影响不显著;博主类型显著影响旅游目的地的认知、情感及整体形象;自我一致性中介作用表明,博主类型和微博账号类型通过间接途径显著影响旅游目的地形象。研究结论不仅丰富了短视频属性对旅游目的地形象研究的内容,而且还可以通过市场细分、大数据分析识别,为旅游形象地管理者塑造形象时提供理论依据,也为其在社交媒体短视频营销的精准投放提供参考。

融合内容特征与传播特征的微博文本情感分类

基于Word2vec的文本向量化表示方法未充分考虑微博文本的内容特征与传播特征,导致文本向量化表示欠佳,且采用单个机器学习算法进行情感分类的精度不高。提出一种融合文本中表情符号,词的语义、词性与情感等内容特征,评论、转发与点赞数等传播特征,共同构建蕴含丰富语义与情感信息的文本特征向量。根据各基分类器在训练数据集上的性能表现设置不同权重,并与类概率向量相乘,保留最大、最小与平均加权概率值,同时结合原始文本特征向量作为元分类器的输入数据以改进原Stacking算法,进行微博文本情感分类。在微博数据集上的实验结果表明:本文方法能更好地表示文本向量,以加权方式改进的Stacking集成学习分类器优于单个分类器;相较于其他情感分类方法,本文方法的准确率提升1.75%~4.90%。

基于改进K-均值的微博热点话题发现方法

微博文本数据高维度、同义、多义特征明显,传统基于向量空间模型(VSM)联合K-均值的热点话题发现方法存在准确率低,计算复杂,聚类中心难以确定等问题。提出一种相关向量机(RVM)优化VSM的微博文本向量化方法,首先利用RVM的自适应特征选择能力对VSM特征向量进行降维,然后利用主成分分析(PCA)方法确定K-均值算法的初始聚类中心,进而采用K-均值算法得到聚类结果,最后根据微博转发、评论和高影响力用户数量定义热度指数,热度指数最大的话题即为当前热点话题。采用实际微博文本数据集开展实验,结果表明所提方法相对于2种传统方法的准确率分别提升7.3%和1.1%,实时性分别提升45%和53%。

突发事件中政务微博的舆情信息工作策略研究——以“9·5”泸定地震为例

文章以“9·5”四川泸定地震为例,以三个地方政务微博为研究对象,采用统计分析法、内容分析法和对比分析法,对事件中地方政务微博的信息响应情况、信息发布内容及文本特点、信息互动情况、信息传播效果四个方面进行分析。结果表明,以受众为中心的舆情信息工作策略对信息传播有积极作用,开展舆情信息工作需注重政务微博群的信息发布协同联动。因此,政务微博在突发事件中应紧贴受众需求,优化信息发布机制;提高服务意识,促进政民双向沟通;强化联动网络,发挥微博共振效应。

基于关键词共现网络的主题词提取算法

针对主题词提取中关键词提取不准确以及仅考虑单一相关性的问题,提出一种将集成思想与复杂网络相结合的主题词提取算法。首先通过集成算法提取话题数据的关键词,以提高关键词提取的准确性,其次改进传统词共现公式计算关键词的共现度,并建立关键词共现网络,在网络的基础上得到最优连通子图,同时以节点度中心性为权重衡量关键词重要性并从中映射出主题词。最后,使用微博话题数据集进行实例验证,证明该算法是有效的,并优于传统的词共现算法,并在青岛社区话题数据集中进行应用。

结合文本与用户信息的微博谣言检测方法研究

微博已成为人们获取信息的重要来源,但微博中也充斥着大量网络谣言,会对个人和社会造成严重危害。由于谣言的模糊性和隐蔽性,导致微博谣言的自动识别非常困难,这给微博监管带来极大挑战。针对微博谣言的文本信息,构建了句子级细粒度的情感分类算法,对微博评论进行情感倾向性分析,计算负面情感评论比例;同时结合微博的用户信息,分析微博转发和评论列表,判断是否有潜在辟谣用户参与,并利用发文用户的等级、信用等信息,定义并计算用户信誉值;最后训练分类器并完成对微博未知样本的预测。实验结果表明,所提出的新特征能有效提高微博谣言检测的准确率。

基于微博大数据的“一路象北”事件社会感知与时空格局研究

2020—2021年“一路象北”(一群云南大象集体向北迁移并返回)事件吸引了国内外媒体的关注。该文对2020年3月1日至2021年11月30日“一路象北”事件微博数据进行时空分析与情感分析,结果表明:(1)该事件属于大众媒体导入性事件,微博评论与微博博文热度趋势大致同步,但最高值的出现时间不同;(2)全国尺度上,东、中、西部的关注热度空间格局各有特点,云南、河南、四川的热度较高,云南省内微博热度集中于大象行经地点,整体空间自相关性不显著;(3)微博博文与评论的文本相似度较低,情感的侧重点略有偏差,利用社会语义网络分析得到的中心、次中心有差异,整体呈现正向情感。该研究可为中长时间尺度的网络事件舆情分析与生态文明建设提供参考。

公共图书馆网络直播服务现状及发展策略研究

文章采用网络调查法,依托新浪微博平台对公共图书馆网络直播现状进行调查,对公共图书馆官方微博粉丝数、直播期数、直播内容、品牌栏目、直播平台、主播来源进行统计与分析,指出公共图书馆网络直播活动存在的问题:直播活动缺乏规范、直播内容与形式单一、直播活动渠道狭窄、缺乏评价指标体系,最后根据问题提出优化网络直播服务的策略,即增强网络直播意识;创新直播服务内容与形式;拓宽直播服务渠道;建立健全网络直播评价指标体系,以期为提升公共图书馆网络直播活动水平提供有益借鉴。