Context Aware Text Classification and Recommendation Model for Toxic Comments Using Logistic RegressionIn recent days, the online conversations have become a vibrant platform in expressing one's opinion about the issues prevailing in the society. Increase of threats, abuses and harassment in social web...
1)Text Classification Traditional text classification works mainly focus on three topics: feature engineering, feature selection and using different types of machine learning algorithms. For feature engineering, the most widely used feature is the bag-of-words feature. In addition, some more complex fe...
重点:The classifier is trained using logistic regression classifier with features from Spark’s standard tokenizer and HashingTF 比较简单就是训练了一个逻辑回归的线性分类器,使用的特征是spark里的HashingTF,正样本是WebText, Wikiedia, and our web books corpus;负样本是unfiltered Common Crawl原始的Common C...
With the explosive growth of Internet information, the classification of massive Internet data plays a very important role in real life. Text classificatio
We use Tencent news titles as our text classification dataset. A total of 8,826 titles of four categories (society, entertainment, healthcare, and military) are extracted. The lengths of titles range from 10 to 20 words. We train ℓ2-regularized logistic regression classifiers using the LIB...
Text classification is a classic topic for natural language processing, in which one needs to assign predefined categories to free-text documents. 文本分类是自然语言处理的一个经典主题,在这个主题中,需要为自由文本文档分配预定义的类别。 The range of text classification research goes from designing the ...
Two of the most popular algorithms for text classification are a Naive Bayes classifier and a logistic regression classifier (sometimes referred to as Maximum Entropy classifier or MaxEnt for short). These two algorithms are both efficient for high-dimensional data and have proven to be among the ...
The classifier is trainable and not limited to logistic regression and can take on any form as long as it performs classification. Figure 4-12. Using the embeddings as our features, we train a logistic regression model on our training data. We will keep this step straightforward and use a ...
A TensorFlow Tutorial: Email Classification(Feb 1, 2016 byJosh Meyer) It contains sample code for feeding customized training data set from csv files. It used a simple logistic regression classifier to classify Emails. A nice tutorial on WildML that uses TensorFlow:Implementing a CNN for Text Cl...
print(classification_report(y_test, y_pred,target_names=my_tags)) 和SVM相比,准确率稍低一些,但还是比朴素贝叶斯分类高4%,约为78% 接下来,我们将用词嵌入和神经网络等更高级的算法来看看准确率 词向量化和逻辑回归 Word2vec and Logistic Regression ...