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摘要:
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.
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篇名 Support Vector Machine for Sentiment Analysis of Nigerian Banks Financial Tweets
来源期刊 数据分析和信息处理(英文) 学科 数学
关键词 SENTIMENT Analysis Support Vector Machine (SVM) NIGERIAN BANKS OPINION Mining Twitter Social Media ANALYTICS
年,卷(期) 2019,(4) 所属期刊栏目
研究方向 页码范围 153-173
页数 21页 分类号 O17
字数 语种
DOI
五维指标
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研究主题发展历程
节点文献
SENTIMENT
Analysis
Support
Vector
Machine
(SVM)
NIGERIAN
BANKS
OPINION
Mining
Twitter
Social
Media
ANALYTICS
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
数据分析和信息处理(英文)
季刊
2327-7211
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
106
总下载数(次)
0
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