一种基于混合特征的中文情感词典扩展方法

覆盖面广且领域适应性好的情感词典可以有效提高文本情感分析效能。设计了基于连词语言特征和词性特征向量统计特征的中文情感词典扩展算法,提出了综合两种方法的混合特征算法。算法计算得到词语的细粒度的积极和消极情感极性值,并对通用情感词典在领域内进行扩展以提高覆盖度,对词典进行领域内调整以提高适应性。实验结果表明,算法在领域内扩展获得的词典比通用情感词典覆盖度和适应性更好,在情感分类任务中性能接近有监督方法。     

一种面向情感分析的微博表情情感词典构建及应用

表情符号作为一种新的网络语言,在微博中被广泛采用,在一定程度上代表了用户的情绪和思想,也将影响微博情感倾向分析的结果。该文提出基于微博统计数据为表情符号构建情感词典的思想,通过对大量微博中与表情＂共现＂的文本的情感倾向分析,确定表情的情感倾向,以此构建面向情感倾向分析的表情情感词典,旨在为微博乃至其它采用表情符号的Web用户生成信息的情感倾向分析提供支持。进而,该文将表情情感词典反作用于对应的微博文本,重新度量其中情感词的倾向值,改进现有的情感词典,旨在获得更准确的情感倾向分析结果。实验表明了该方法的有效性,并分析了相关阈值的设置对结果的影响。     

一种基于情感词典和朴素贝叶斯的中文文本情感分类方法*

基于朴素贝叶斯理论提出了一种新的中文文本情感分类方法。这种方法利用情感词典对文本进行处理和表示,基于朴素贝叶斯理论构建文本情感分类器,并以互联网上宾馆中文评论作为分类研究的对象。实验表明,使用提出的方法构成的分类器具有分类速度快、分类准确度高、鲁棒性强等特点,并且适合于大量中文文本情感分类应用系统。     

一种基于语义相似性的情感分类方法

情感词典有助于情感分析,可以通过词语匹配来进行情感分类。但是,情感词典在词汇覆盖和领域适应方面存在一定的局限性。为此,文中提出了一种基于语义相似性度量和嵌入表示的情感分类方法,该方法计算了待分类文本与情感词典之间的语义相似度,将语义距离和基于嵌入的特征结合起来进行情感分类,有助于解决语义特征利用不足的问题。文中分别采用词向量、情感词典匹配和所提方法提取的特征向量来对情感分类性能进行了评估。实验结果表明,所提方法整体上优于对比方法。在3种电商评论测试语料中,所提方法的F1平均值达到了83.46%,相比对比方法提升了8.26%。其中,利用词嵌入与ECSD(E-Commerce Sentiment Dictionary)相结合提取的语义分类效果最佳,性能提升达到了9%,表明通过结合语义相似度可以丰富提取的情感语义特征,能够有效提升情感分类的性能。     

基于情感词典与机器学习的旅游网络评价情感分析研究

针对旅游网络评价使用的旅游情感词汇量不多的特点,提出一种基于旅游情感词典和机器学习相结合的方法,用于旅游网络点评的情感倾向性分析研究。采用向量空间模型表示旅游评价文本,使用旅游情感词典对特征空间进行降维,采用TF-IDF特征权重法计算权重,利用SVM机器学习模型对评价进行分类,实验结果表明,该方法能够有效地进行旅游网络评价分类。     

基于时间序列模型和情感分析的情感趋势预测

为解决传统情感分析方法无法对公众未来情感走势变化有效预测的问题,提出一种将时间序列模型与情感分析相结合的情感趋势预测方法.采用深度学习模型对股市论坛实时评论信息进行情感分类,统计固定时间单位的情感值,构建情感值时间序列,提出ARIMA-GARCH时间序列模型,对情感值时间序列进行建模分析,预测投资者的情感走势.实验结果表明,该方法对于情感趋势的预测结果合理,误差较小.同时,发现投资者情感趋势与股市涨跌幅走势相似,为投资决策提供了参考.     

一种基于OCC模型的文本情感挖掘方法

观点挖掘（或情感分析）作为面向网络社会媒体分析挖掘领域的一个核心研究课题,具有重要的研究意义和应用价值。针对传统观点挖掘方法存在的不足和局限性,本文设计并实现了一种基于OCC情感模型的观点挖掘方法。该方法首先采用统计方法,利用WordNet词典、句法依存关系及少量标注数据,自动构建情感维度词典;其次,对所构建的情感维度词典进行求精,通过语义、情感倾向的不一致性处理和非情感词的过滤,得到高质量的情感维度词典;最后,基于所得到的情感维度词典,结合OCC模型中情感维度值与情感类型的对应关系,生成6种主要的情感类型。实验方法表明,此方法在使用灵活性、可解释性和有效性上具有明显的优势。     

基于情感轮和情感词典的文本情感分布标记增强方法

情感分布学习是一种近年提出的用于处理存在情绪模糊性的多情绪分析模型,其核心思路是通过情感分布记录示例在各个情绪上的表达程度.不同于传统的单标记或多标记学习,情感分布学习可以定量地对多个情绪同时建模.目前,情感分布学习面临的一个重要困难是缺乏已标注情感分布的文本数据集.为了利用大量已有的单标记情感数据集,情感分布标记增强...     

基于情感词典的酒店评论情感分析与可视化

为研究高端酒店服务中的亮点和不足,分析酒店用户评论舆情,文章对高端酒店用户评论进行情感分析和可视化,提出酒店优势与改进策略。文章采用通用情感词典Hownet与酒店评论相关的评论领域专业词典相结合的方式构建领域情感词典。结合所构建的领域情感词典和其他特殊词典,比如短语词典、否定词词典和副词词典等进行情感分类,然后将分类完成的三个极性的情感词进行词频统计和词云绘制,最后根据词云结果,给出高端酒店在经营策略上的改进建议。     

基于情感常识的微博事件公众情感趋势预测

微博日益成为一个巨大而复杂的互联网舆论平台。分析微博中特定话题的情感趋势对于了解网络舆情、分析产品销量趋势显得尤为重要。该文使用微博进行真实事件公众情感趋势预测: 首先,考虑到微博特征稀疏、上下文缺失的特性,借助词语上下位语义关系对其进行语义扩充;其次,使用语义特征和情感常识知识构造双层分类方法进行情感分析;最后,对特定事件在连续时间段内的微博使用时序情感分析方法进行公众情感趋势预测。实验证明,该情感分析方法准确率相对于传统分类方法有明显的提高,在此基础上的情感趋势预测符合事件的真实发展状况。     

基于股票市场灵敏度分析的神经网络预测模型

股票市场是非线性系统,具有内部结构复杂性和外部因素多变性,建立基于股票市场灵敏度分析的神经网络预测模型。针对神经网络结构设计问题,计算网络输入层与隐层神经元的灵敏度,并修剪网络中不敏感的神经元,在保证模型泛化能力的同时,实现网络结构精简;针对神经网络黑箱问题,根据输入层神经元灵敏度解决各输入变量对股票市场的重要性和反馈机制。以上证指数为例,在不同的时间跨度下对股票市场运行规律进行学习,并分析不同结构修剪模型的适用性和市场意义。最后,通过与其他神经网络预测模型比较,验证本文模型的有效性。     

基于理性指标的马尔可夫链股市态势预测方法

由于股票市场存在人为扰动性,使得基于情绪的股市预测算法效果不佳。针对股市的诱多诱空问题,提出一种基于理性指标的马尔可夫链股市态势预测算法（RI_MCA）。提取股市的主要理性特征,并对这些理性特征进行量化;通过主成分分析将这些理性特征融合成理性指标,并利用理性指标获取股市的买卖点;将买卖点所对应的股市状态引入到马尔可夫链中,实现股市态势预测。在理性指标和股市状态相背离情况下会降低买卖点的可靠性,因而通过将特征背离引入到RI_MCA算法中提出了RICD_MCA算法,RICD_MCA算法根据特征背离程度对RI_MCA算法的结果进行调整优化。在上证指数上的实验比较与分析结果表明,RICD_MCA算法具有更高的预测精度。     

A GA-weighted ANFIS model based on multiple stock market volatility causality for TAIEX forecasting

Stock market forecasting is important and interesting, because the successful prediction of stock prices may promise attractive benefits. The economy of Taiwan relies on international trade deeply, and the fluctuations of international stock markets will impact Taiwan stock market. For this reason, it is a practical way to use the fluctuations of other stock markets as forecasting factors for forecasting the Taiwan stock market. In this paper, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs a genetic algorithm (GA) to refine the weights of rules joining in an ANFIS model to forecast the Taiwan stock index. To evaluate the forecasting performances, the proposed model is compared with four different models: Chen's model, Yu's model, Huarng's model, and the ANFIS model. The results indicate that the proposed model is superior to the listing methods in terms of the root mean squared error (RMSE).     

A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting

In this paper we propose and validate a trading rule based on flag pattern recognition, incorporating important innovations with respect to the previous research. Firstly, we propose a dynamic window scheme that allows the stop loss and take profit to be updated on a quarterly basis. In addition, since the flag pattern is a trend-following pattern, we have added the EMA indicator to filter trades. This technical analysis indicator is calculated both for 15-min and 1-day timeframes, which enables short and medium terms to be considered simultaneously. We also filter the flags according to the price range on which they are developed and have limited the maximum loss of each trade to 100 points. The proposed methodology was applied to 91,309 intraday observations of the DJIA index, considerably improving the results obtained in the previous proposals and those obtained by the buy & hold strategy, both for profitability and risk, and also after taking into account the transaction costs. These results seem to challenge market efficiency in line with other similar studies, in the specific analysis carried out on the DJIA index and is also limited to the setup considered.     

融合微博情感分析和深度学习的宏观经济预测方法

现代市场经济快速发展的同时也伴随着较高的风险,通过对地区投资情况提前预测,能够提前发现投资风险,为国家、企业的投资决策提供参考。针对宏观经济预测中统计数据滞后和内部关系复杂的问题,提出融合情感分析和深度学习的预测方法（SA-LSTM）。首先考虑微博的强时效性,确定了微博爬取和情感分析的方法,得到微博情感分析的分值,进而结合政府统计的结构化经济指标和长短期记忆神经网络,实现地区投资总额预测。经过实际数据计算验证,在四个数据集上,与不加入微博情感分析的LSTM网络相比,SA-LSTM能够降低预测相对误差4.95,0.92,1.21,0.66个百分点;与差分自回归移动平均模型（ARIMA）、线性回归（LR）、反向传播（BP）神经网络、长短期记忆（LSTM）网络四个方法中的最优方法相比能够降低相对误差0.06,0.92,0.94,0.66个百分点。另外,SA-LSTM在多个时间片上,预测相对误差的方差最小,表明所提方法具有很好的鲁棒性,对数据抖动有良好的适应性。     

基于集成情感成员模型的文本情感分析方法

文本情感分类是指通过挖掘和分析文本中的观点、意见和看法等主观信息,对文本的情感倾向做出类别判断。基于集成情感成员模型提出一种文本情感分析方法。把基于改进的神经网络、基于语义特征和基于条件随机场的三个情感分类模型作为成员模型集成在一起。集成后的模型能够涵盖不同的情感特征,从而克服了传统集成学习中仅关注成员模型处理结果的不足。以公开语料进行实验,集成模型融合了多个成员模型的优势,分类正确率达到了88.2%,远高于任一成员模型的效果。     

Applying BERT to analyze investor sentiment in stock market

Neural Computing and Applications - This paper is an analysis of investor sentiment in the stock market based on the bidirectional encoder representations from transformers (BERT) model. First, we...     

基于Agent的投资者情绪对于股市演化行为仿真研究

基于双向拍卖机制作为价格生成机制,应用遗传算法来进化预测规则,建立了中国股市的人工金融市场模型,并在此基础上研究了投资者情绪对于市场演化行为的影响。研究结果表明人工市场能够产生真实市场演化过程中的混沌动力学行为,并且市场演化行为随着投资者情绪的变化而变动。这一研究对挖掘中国股票市场的演化规律具有重要意义。     

A novel stock recommendation system using Guba sentiment analysis

Investment recommendation has been one of the hottest topics in the finance area which can help investors to get more profits and to avoid loss. Existing recommendation systems mostly depend on analysis of trading data and company profit prediction. Though many works show that there is a positive correlation between investors’ sentiment and the finance market trends, few recommendation theories have been built based on sentiment. The primary reason is the difficulty to measure investors’ sentiment. In this work, a novel stock recommendation system is developed based on a proposed theory concerning the correlation between Guba-based sentiment of the retail investors and the stock market trends in China. To verify four hypotheses of the theory, a novel method is proposed to measure the investors’ sentiment by exploiting the large volumes of emotion enriched texts posted in Guba, which is online social platform for individual investors to share news and opinions concerning their favorite stocks. Results show the correctness of the proposed theory: (1) there is a positive correlation between Guba-based sentiment and the stock market trends; 2) the higher the post volumes and agreement, more proficiency the bullishness would be; and (3) a long-lasting negative Guba-based sentiment indicates the arrival of the bear market. The proposed recommendation system consists of three criteria accordingly to ensure the portfolio to meet requirements of the theory. Finally, experiments are implemented using the real data of Chinese stock market from March 2009 to March 2016 and the results show the effectiveness of the proposed system in recommending lucrative stocks and the theoretical cumulate profit is about eight times of the CSI300 in the period.     

A Bayesian regularized artificial neural network for stock market forecasting

In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. The accurate prediction of stock price movements could play an important role in helping investors improve stock returns. The complexity in predicting these trends lies in the inherent noise and volatility in daily stock price movement. The Bayesian regularized network assigns a probabilistic nature to the network weights, allowing the network to automatically and optimally penalize excessively complex models. The proposed technique reduces the potential for overfitting and overtraining, improving the prediction quality and generalization of the network. Experiments were performed with Microsoft Corp. and Goldman Sachs Group Inc. stock to determine the effectiveness of the model. The results indicate that the proposed model performs as well as the more advanced models without the need for preprocessing of data, seasonality testing, or cycle analysis.