Sentiment analysis (supervised and unsupervised polarity classification) of Twitter data about the Albania 2019 earthquake

This database contains the comparison of results between the supervised and
unsupervised polarity classification of the tweets related to the 2019 Albania earthquake.
The database was constructed with the aim to test the accuracy of the algorithm
developed by MonkeyLearn for polarity classification.

675 Tweets with the hashtags: #Albania
#AlbanianEarthquake #albanianearthquake from the 26th November 2019 to the 3rd
February 2020 were collected by the third-party vendor: TweetBinder. The social
media department of Newcastle University provided us with 1001 tweets with the
hashtags: #Albania collected from the 31st January to the 2nd February 2020. After
removing repeated tweets from the database, we obtained a dataset made up of 255
original tweets. This database only contains text data of original tweets (no
retweets). Attributes and data contained:

-N: Number of the tweet

-Tweet: Text data

-ML-Classification: Unsupervised polarity classification performed by the
algorithm developed by MonkeyLearn.

-Confidence: Percentage of trust that the predicted polarity is right

-RB-Classification: Supervised polarity classification performed by experts.

-Accuracy: Coincidence indicated by 1 in polarity predicted by the
unsupervised classification and the supervised classification. No coincidence
is indicated by 0.

-TPt: true positive

-FPtNg: misclassified as Positive then False positive, when it is negative

-FPtNt: misclassified as Positive then False positive , when it is Neutral

-TNg: true negative

-FNgPt: missclasified as Negative then False negative when it is Postive.

-FNgNt: missclasified as Negative then False negative when it is Neutral.

-TNt: true neutral

-FNt Pt: missclasified as Neutral, then False-Neutral when it is Positive

-FNtNg: and missclasified as Neutral, then False-Neutral, when it is
negative.

本数据库收录了关于2019年阿尔巴尼亚地震的推文在监督学习和无监督学习极性分类结果之间的比较。该数据库的构建旨在检验MonkeyLearn开发的人工智能极性分类算法的准确性。数据库中收集了自2019年11月26日至2020年2月3日的675条带有#Albania、#AlbanianEarthquake和#albanianearthquake标签的推文，由第三方供应商TweetBinder提供。纽卡斯尔大学社交媒体部门向我们提供了1001条带有#Albania标签的推文，收集时间为2020年1月31日至2月2日。在移除数据库中的重复推文后，我们得到了由255条原始推文组成的数据库。本数据库仅包含原始推文的文本数据（不含转发）。数据集包含以下属性和内容：
-N：推文编号
-Tweet：文本数据
-ML-Classification：由MonkeyLearn开发的算法执行的无监督极性分类
-Confidence：预测极性的正确性可信度百分比
-RB-Classification：由专家执行的有监督极性分类
-Accuracy：无监督分类和有监督分类预测的极性一致性，一致时以1表示，不一致时以0表示
-TPt：真正例
-FPtNg：误分类为正面，实际为负面的假阳性
-FPtNt：误分类为正面，实际为中性的假阳性
-TNg：真负例
-FNgPt：误分类为负面，实际为正面的假阴性
-FNgNt：误分类为负面，实际为中性的假阴性
-TNt：真中性
-FNt Pt：误分类为中性，实际为正面的假中性
-FNtNg：误分类为中性，实际为负面的假中性。