用于情绪分析的好数据集? [英] Good dataset for sentiment analysis?

问题描述

我正在进行情绪分析，我正在使用此链接中给出的数据集:http://www.cs.jhu.edu/~mdredze/datasets/sentiment/index2.html 我已将我的数据集划分为 50:50 的比例.50%作为测试样本，50%作为训练样本，从训练样本中提取特征并使用Weka分类器进行分类，但我的预测准确率在70-75%左右.

I am working on sentiment analysis and I am using dataset given in this link: http://www.cs.jhu.edu/~mdredze/datasets/sentiment/index2.html and I have divided my dataset into 50:50 ratio. 50% are used as test samples and 50% are used as train samples and the features extracted from train samples and perform classification using Weka classifier, but my predication accuracy is about 70-75%.

任何人都可以建议一些其他数据集来帮助我提高结果 - 我使用 unigram、bigram 和 POStags 作为我的特征.

Can anybody suggest some other datasets which can help me to increase the result - I have used unigram, bigram and POStags as my features.

推荐答案

获取情感分析数据集的来源很多:

There are many sources to get sentiment analysis dataset:

• 来自谷歌的巨大 ngrams 数据集 storage.googleapis.com/books/ngrams/books/datasetsv2.html
• http://www.sananalytics.com/lab/twitter-sentiment/
• http://inclass.kaggle.com/c/si650winter11/data
• http://nlp.stanford.edu/sentiment/treebank.html
• 或者您可以查看这个全球机器学习数据集存储库:https://archive.ics.uci.edu/ml

无论如何，这并不意味着它会帮助您获得更好的当前数据集准确性，因为语料库可能与您的数据集有很大不同.除了降低与训练相比的测试百分比之外，您还可以:测试其他分类器或使用 CVParameterSelection 或 GridSearch 等半自动包装器微调所有超参数，如果合适的话，甚至可以使用 auto-weka.

Anyway, it does not mean it will help you to get a better accuracy for your current dataset because the corpus might be very different from your dataset. Apart from reducing the testing percentage vs training, you could: test other classifiers or fine tune all hyperparameters using semi-automated wrapper like CVParameterSelection or GridSearch, or even auto-weka if it fits.

很少使用 50/50，80/20 是相当普遍的比率.更好的做法是使用:60% 用于训练，20% 用于交叉验证，20% 用于测试.

It is quite rare to use 50/50, 80/20 is quite a commonly occurring ratio. A better practice is to use: 60% for training, 20% for cross validation, 20% for testing.

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