The rise of social media paves way for unprecedented benefits or risks to several organisations depending on how they adapt to its changes. This rise comes with a great challenge of gaining insights from these big dat...The rise of social media paves way for unprecedented benefits or risks to several organisations depending on how they adapt to its changes. This rise comes with a great challenge of gaining insights from these big data for effective and efficient decision making that can improve quality, profitability, productivity, competitiveness and customer satisfaction. Sentiment analysis is the field that is concerned with the classification and analysis of user generated text under defined polarities. Despite the upsurge of research in sentiment analysis in recent years, there is a dearth in literature on sentiment analysis applied to banks social media data and mostly on African datasets. Against this background, this study applied machine learning technique (support vector machine) for sentiment analysis of Nigerian banks twitter data within a 2-year period, from 1st January 2017 to 31st December 2018. After crawling and preprocessing of the data, LibSVM algorithm in WEKA was used to build the sentiment classification model based on the training data. The performance of this model was evaluated on a pre-labelled test dataset generated from the five banks. The results show that the accuracy of the classifier was 71.8367%. The precision for both the positive and negative classes was above 0.7, the recall for the negative class was 0.696 and that of the positive class was 0.741 which shows the prediction did better than chance in addition to other measures. Applying the model in predicting the sentiments of the five Nigerian banks twitter data reveals that the number of positive tweets within this period was slightly greater than the number of negative tweets. The scatter plots for the sentiments series indicated that, majority of the data falls between 0 and 100 sentiments per day, with few outliers above this range.展开更多
股市的情绪化倾向是股票市场具有高度不确定性的主要原因,直接利用历史数据的股票趋势预测方法难以适应市场情绪的多变性,在实际应用中效果不理想。文章针对市场情绪的不稳定性导致股市拐点难以预测的问题,提出一种基于情绪向量的隐半...股市的情绪化倾向是股票市场具有高度不确定性的主要原因,直接利用历史数据的股票趋势预测方法难以适应市场情绪的多变性,在实际应用中效果不理想。文章针对市场情绪的不稳定性导致股市拐点难以预测的问题,提出一种基于情绪向量的隐半马尔可夫模型股市拐点预测方法(hidden semi-Markov model stock turning point prediction method based on sentiment vector,SV-HSMM)。针对市场情绪不可观察性,选取与市场情绪相关的主要特征,使用马尔可夫毯融合成市场情绪;利用隐半马尔可夫模型建模市场环境,构建市场情绪、市场状态和状态持续时间之间的结构关系;引入情绪向量平滑情绪的多变性,并利用Kullback-Leibler(KL)距离量化情绪热度;利用隐半马尔可夫模型的动态推理实现股市拐点预测。结果表明情绪向量方法具有更好的预测效果。展开更多
External factors, such as social media and financial news, can have wide-spread effects on stock price movement. For this reason, social media is considered a useful resource for precise market predictions. In this pa...External factors, such as social media and financial news, can have wide-spread effects on stock price movement. For this reason, social media is considered a useful resource for precise market predictions. In this paper, we show the effectiveness of using Twitter posts to predict stock prices. We start by training various models on the Sentiment 140 Twitter data. We found that Support Vector Machines (SVM) performed best (0.83 accuracy) in the sentimental analysis, so we used it to predict the average sentiment of tweets for each day that the market was open. Next, we use the sentimental analysis of one year’s data of tweets that contain the “stock market”, “stocktwits”, “AAPL” keywords, with the goal of predicting the corresponding stock prices of Apple Inc. (AAPL) and the US’s Dow Jones Industrial Average (DJIA) index prices. Two models, Boosted Regression Trees and Multilayer Perceptron Neural Networks were used to predict the closing price difference of AAPL and DJIA prices. We show that neural networks perform substantially better than traditional models for stocks’ price prediction.展开更多
通过基于预训练转换器(Transformer)双向编码器表征的文本卷积神经网络(text convolutional neural network model based on pre-training bidirectional encoder representations from transformer,BERT-TextCNN)模型实现汽车网站评论...通过基于预训练转换器(Transformer)双向编码器表征的文本卷积神经网络(text convolutional neural network model based on pre-training bidirectional encoder representations from transformer,BERT-TextCNN)模型实现汽车网站评论的情感分析,其目的在于研究用户对汽车产品和汽车服务的情感态度。首先采用基于Transformer的双向编码器表征(bidirectional encoder representations from transformer,BERT)模型能有效解决汽车评论中存在的一词多义问题,并产生包含丰富信息的动态词向量,然后结合文本卷积神经网络(text convolutional neural network,TextCNN)模型中的卷积运算和池化运算提取关键特征,最后通过softmax函数计算评论文本情感的概率分布。试验结果表明,BERT-TextCNN模型在情感分类中相比几种常见的神经网络模型的精度、召回率和F1值均有所提升。展开更多
文摘The rise of social media paves way for unprecedented benefits or risks to several organisations depending on how they adapt to its changes. This rise comes with a great challenge of gaining insights from these big data for effective and efficient decision making that can improve quality, profitability, productivity, competitiveness and customer satisfaction. Sentiment analysis is the field that is concerned with the classification and analysis of user generated text under defined polarities. Despite the upsurge of research in sentiment analysis in recent years, there is a dearth in literature on sentiment analysis applied to banks social media data and mostly on African datasets. Against this background, this study applied machine learning technique (support vector machine) for sentiment analysis of Nigerian banks twitter data within a 2-year period, from 1st January 2017 to 31st December 2018. After crawling and preprocessing of the data, LibSVM algorithm in WEKA was used to build the sentiment classification model based on the training data. The performance of this model was evaluated on a pre-labelled test dataset generated from the five banks. The results show that the accuracy of the classifier was 71.8367%. The precision for both the positive and negative classes was above 0.7, the recall for the negative class was 0.696 and that of the positive class was 0.741 which shows the prediction did better than chance in addition to other measures. Applying the model in predicting the sentiments of the five Nigerian banks twitter data reveals that the number of positive tweets within this period was slightly greater than the number of negative tweets. The scatter plots for the sentiments series indicated that, majority of the data falls between 0 and 100 sentiments per day, with few outliers above this range.
文摘股市的情绪化倾向是股票市场具有高度不确定性的主要原因,直接利用历史数据的股票趋势预测方法难以适应市场情绪的多变性,在实际应用中效果不理想。文章针对市场情绪的不稳定性导致股市拐点难以预测的问题,提出一种基于情绪向量的隐半马尔可夫模型股市拐点预测方法(hidden semi-Markov model stock turning point prediction method based on sentiment vector,SV-HSMM)。针对市场情绪不可观察性,选取与市场情绪相关的主要特征,使用马尔可夫毯融合成市场情绪;利用隐半马尔可夫模型建模市场环境,构建市场情绪、市场状态和状态持续时间之间的结构关系;引入情绪向量平滑情绪的多变性,并利用Kullback-Leibler(KL)距离量化情绪热度;利用隐半马尔可夫模型的动态推理实现股市拐点预测。结果表明情绪向量方法具有更好的预测效果。
文摘External factors, such as social media and financial news, can have wide-spread effects on stock price movement. For this reason, social media is considered a useful resource for precise market predictions. In this paper, we show the effectiveness of using Twitter posts to predict stock prices. We start by training various models on the Sentiment 140 Twitter data. We found that Support Vector Machines (SVM) performed best (0.83 accuracy) in the sentimental analysis, so we used it to predict the average sentiment of tweets for each day that the market was open. Next, we use the sentimental analysis of one year’s data of tweets that contain the “stock market”, “stocktwits”, “AAPL” keywords, with the goal of predicting the corresponding stock prices of Apple Inc. (AAPL) and the US’s Dow Jones Industrial Average (DJIA) index prices. Two models, Boosted Regression Trees and Multilayer Perceptron Neural Networks were used to predict the closing price difference of AAPL and DJIA prices. We show that neural networks perform substantially better than traditional models for stocks’ price prediction.
文摘通过基于预训练转换器(Transformer)双向编码器表征的文本卷积神经网络(text convolutional neural network model based on pre-training bidirectional encoder representations from transformer,BERT-TextCNN)模型实现汽车网站评论的情感分析,其目的在于研究用户对汽车产品和汽车服务的情感态度。首先采用基于Transformer的双向编码器表征(bidirectional encoder representations from transformer,BERT)模型能有效解决汽车评论中存在的一词多义问题,并产生包含丰富信息的动态词向量,然后结合文本卷积神经网络(text convolutional neural network,TextCNN)模型中的卷积运算和池化运算提取关键特征,最后通过softmax函数计算评论文本情感的概率分布。试验结果表明,BERT-TextCNN模型在情感分类中相比几种常见的神经网络模型的精度、召回率和F1值均有所提升。
