The algorithm text classification effect has been significantly improved. Key words : text classification;VSM;TF-IDF;petroleum;support vector machine 0 引言 TF-IDF算法结构简单,类别区分力强,且容易实现,被广泛应用于信息检索、文本挖掘、文本分类、信息
In the Brown corpus, the average accuracy, completeness, and F1 value of the designed algorithm were 96.2 %, 91.2 %, and 93.2 %, respectively. When the number of online customers reached 1000, the response time of the designed Chinese system was 1.15 s, the classification recommendation ...
extract_tags函数参数介绍如下: def extract_tags(self, sentence, topK=20, withWeight=False, allowPOS=(), withFlag=False):"""Extract keywords from sentence using TF-IDF algorithm.Parameter:- topK: return how many top keywords. `None` for all possible words.- withWeight: if True, return a l...
Extract keywords from sentence using TF-IDF algorithm. Parameter: - topK: return how many top keywords. `None` for all possible words. - withWeight: if True, return a list of (word, weight); if False, return a list of words. - allowPOS: the allowed POS list eg. ['ns', 'n', '...
基于内容的推荐算法(Content-BasedRecommendationAlgorithm)是一种个性化推荐技术,它主要依赖于用户的历史行为和物品的特征信息来为用户推荐相似的物品。这种算法的核心思想是,如果用户过去喜欢某类物品,那么推荐系统会寻找与这些物品具有相似特征的其他物品推荐给用户。 2.1.1特征提取 在基于内容的推荐算法中,首先需要从物品...
TF-IDF(term frequency–inverse document frequency)是一种用于信息检索与数据挖掘的常用加权技术。TF是词频(Term Frequency),IDF是逆文本频率指数(Inverse Document Frequency)。 TF-IDF是一种统计方法,用以评估一字词对于一个文件集或一个语料库中的其中一份文件的重要程度。字词的重要性随着它在文件中出现的次数成...
Extract keywords from sentence using TF-IDF algorithm. Parameter: - topK: return how many top keywords. `None` for all possible words. - withWeight: if True, return a list of (word, weight); if False, return a list of words.
TF-IDF 算法通过分配权重来反映每个词的重要程度,根据权重对一篇文章中的所有词语从高到低进行排序,权重越高说明重要性越高,排在前几位的词就可以作为这篇文章的关键词。所以 TF-IDF 算法可以用来提取关键词。 TF-IDF 全称为term frequency–inverse document frequency ...
TF-IDF(term frequency–inverse document frequency)是一种用于信息检索与数据挖掘的常用加权技术,常用于挖掘文章中的关键词,而且算法简单高效,常被工业用于最开始的文本数据清洗。 TF-IDF有两层意思,一层是"词频"(Term Frequency,缩写为TF),另一层是"逆文档频率"(Inverse Document Frequency,缩写为IDF)。
* Note: This algorithm is improved on the base of the parallel sorting by regularsampling(PSRS). pSort返回值是: *@returnf0: dataset which is indexed by partition id, f1: dataset which has partition id and count. pSort中又分如下几步 ...