面向特定领域的互联网舆情分析技术研究
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摘要
随着互联网技术的飞速发展,网络上的信息呈指数级增长,同时web2.0的交互性技术使人们能够在互联网上进行交流和发表各种意见评论信息,因此互联网上存在各种各样的舆情信息,在信息海洋中,信息处于淹没状态,给人们查找所需信息带来极大困难,如何获取网络上关于特定领域主题事件的舆情信息?聚焦爬虫技术与情感分析技术相结合使我们对特定领域的舆情分析成为可能。通过分析特定领域的网络舆情可以为相关决策部门提供辅助决策支持,有助于企业改进方案计划,为用户提供有用的帮助与导向信息。本文针对其中的一些关键技术和理论方法作了如下三个方面的工作：
(1)提出了基于综合价值具有增量特性的主题爬虫。在主题相关信息采集方面,以往的爬虫在满足爬全率(recall)的同时牲牺了爬准率(harvest)以及爬行效率,而为了提高爬准率往往又降低了爬全率。通过采用前后端分类器,前端基于链接语境图训练链接预测分类器,使爬虫具有一定的穿越隧道的能力,后端使用主题内容分类器识别主题相关网页,同时使用网页内容可视化分块技术,并基于链接的综合价值进行网页预测,提高了爬全率、爬准率及爬行效率。
(2)提出了基于无监督聚类的PU文本分类方法。传统的机器学习文本分类模型需要大量的标注语料做为训练集,PU文本分类算法是解决某些机器学习中训练样本获取代价过大,尤其是反例样本较难获取的实际问题,而传统的分类算法大都需要正例和反例数据集才能取得良好的效果,因此要使用传统的分类方法来解决面向PU的分类问题,U集中可信反例的提取是分类器能够取得良好效果的关键,本文提出了有效的可信反例提取算法(基于聚类的可信反例提取算法)-CBRN,并对已有的PU文本分类算法进行了改进并提出了SPY-SVM算法,提高了可信反例提取的数量和准确率,也提高了PU文本分类的准确率。
(3)评价挖掘是针对特定领域主题的主观性文本自动提取有用的情感信息和知识,可为政府部门、企业及用户提供有价值的意见信息。本文针对中文文本进行褒、贬情感倾向性分析,提出了三种情感倾向性分析算法,1)基于规则及情感词提取评价四元组的评价挖掘算法和基于unigram+评价短语特征的机器学习评价挖掘算法,2)基于字符串核函数的评价挖掘算法,3)基于规则及聚合模型的句子级到篇章级的中文评价挖掘算法。
With the rapid development of Internet technologies, information in web mounts up exponentially. In the meanwhile, interaction technologies of web2.0 enable people to communicate on the Internet and post variety opinions and comments. There has been a variety of public sentiment information on the Internet. Therefore people are facing great difficulties in searching for desired information because the information is always hidden in information ocean. How to get public sentiment information about domain-specific events? The combination of focused crawler technology and sentiment analysis technology make it possible to resolve this problem. By analyzing public sentiment information in specific domain can support decision making of policy-making departments, help enterprises improve program plans, and provide users with useful information. To meet these demands, this dissertation proposes a lot of key techniques, theories and methods as shown in the following three sections:
1. The dissertation proposes Focus Crawler with incremental capability based on synthetic estimate value. Subjects on the web are distributed interweavedly, but the same subject on web has certain distribution rules. We summarize these rules as Hub, Sibling/Linkage Locality, Site subject, Tunnel, Topic trap. We design Focus Crawlers based on the proposed rules. Recent years have witnessed a lot of research on focus crawlers. However, these studies have some limitations. They improved recall at the cost of sacrificing harvest and efficiency. On the other hand, recall would decrease if harvest were satisfied. In this dissertation, we propose front-end/back-end classifiers as the part of link's topic-relevance forecasting. The front-end classifier trains classification model based on linkage context graph, uses the webpage visualized content block partition technique, and predicts whether the link of webpage is topic-relevance based on link's synthetic values. It endows focus crawlers with the ability of going through tunnel, i.e., enables focused crawler to start from some topic-relevant webpage, pass through some irrelevant webpage and reach other topic-relevant webpage. The back-end classifier is used to recognize topic-relevant WebPages based on text content of WebPages. The experimental results show that our focused crawler can dramatically improve recall rate, harvest rate and efficiency.
2. The PU-Oriented Text Classifier Based on Unsupervised Clustered Learning Algorithm is proposed. Traditional text classification models are based on machine learning and need a large amount of labeled corpus as train datasets. So a large number of labeled training documents/webpages (often negative training data) are needed to build accurate classifiers. In text classification, the labeling is typically done manually by reading the documents/webpages, which is a labor-intensive and time-consuming process. Collecting negative training examples is especially painstaking and tedious because (1) negative training examples must uniformly represent the universal set except the positive class (e.g., sample of a nonhomepage should represent the Internet uniformly excluding the homepages), and (2) manually collecting negative training examples tends to cause unconscious bias because of human's unintentional prejudice, which could deteriorate classification performance such as accuracy, precision, recall, etc. PU-Oriented text classifier aims to solve the problems in machine learning that no labeled negative documents are available in the training example set or negative examples are very difficult to collect. Traditional classification algorithm cannot obtain good performance without sufficient positive and negative training dataset. When using traditional classifier to conduct PU-oriented text classification, the key is the extraction of reliable negative training example from unlabeled documents/webpages. The PU-oriented text classification based on machine learning often adopts a two-step approach by making use of both positive and unlabeled examples. At the first step, a lot of reliable negative documents are identified. At the second step, the classifiers are constructed iterative based on training datasets. In this dissertation, the clustering based reliable negative example extraction (CBRN) algorithm is proposed. The number and the accuracy of reliable negative examples extraction is improved. Existing classification is improved, which builds a set of classifiers by iterative applying the SPY-SVM algorithm. This approach randomly selects s% of the documents from the positive set P as the spies and add them into unlabeled datasets. These spies can help improve the accuracy of identifying the negative from unlabeled datasets, and train the classifier iterative until termination condition meets. Experimental results show that our method outperforms other algorithms in terms of accuracy, recall, precision and Fl-measure.
3. Opinion mining or sentiment analyzer is to extract sentiment (or opinion) about a subject from online subjective text documents. At first it classifies the sentiment of an entire document about a subject. It can provide valuable information for government, enterprise and users. The dissertation proposes three semantic orientation analysis (positive, negative and neutral semantic orientation analysis) algorithms for Chinese text. These three methods are described as below:
1) Polarity Classification of Public Health Opinions in Chinese text. With frequently bursting of public health events over the world, people are increasingly expressing their views on these events online. Government agencies need to response and make policies according to these views. We study Chinese opinion mining under the context of public health. This dissertation proposes two complementary approaches-a sentiment word based approach and a machine learning approach. The Chinese sentiment word based approach extracts an opinion quadruple from each single sentence based on rules. We notice that different types of sentences have different contributions to the overall polarity and take into account three types of sentences:common sentences, first-person sentences, topical sentences. We give different weights to these three types of sentences when synthesizing the overall polarity scores of entire review through weighted average. The machine learning based approach extracts unigrams and opinion phrase features by labeling train datasets, selects features by information gain method and train sentiment classification model using ten-fold cross validation. The experiment results show that both methods achieve good performance.
2) This dissertation proposes a string kernel based approach for sentiment classification on Chinese reviews. Machine learning based sentiment classification approaches depend on a feature vector which represents a text. They usually utilize words or n-grams as features and construct feature vectors according to their presence/absence or frequencies. They use these feature vectors to construct sentiment classification model. The selection of feature set is considered as the most important point in classifying documents. The features extract module not only needs comprehensive experts'knowledge, but also ignores the information on word positions, i.e., may lost important information when extracting features such as the position of words and mutual information between words. The word order is extremely important to sentiment analysis. This dissertation proposes sentiment classification for Chinese reviews using machine learning methods based on string kernel. The features are all possible ordered subsequences of characters. It can construct sentiment classification model if important information are not lost. We conduct experiments to show the power of our approach as well.
3) Sentiment analysis of Chinese documents from sentence to document level:This dissertation proposes a rule-based approach including two phases:first, determining each sentence's sentiment based on word dependency and context modifier component, second, aggregating sentences polarity scores to predict the document sentiment. We assign sentences with different weights to adjust their contribution to the overall polarity based on five features, including position of the sentence, weight term/tf-isf weighted of the sentence, the similarity between the sentence and the headline, the occurrence of keywords in the sentence, and the first-person mode. We report the experimental results of comparing our approaches with three machine learning-based approaches based on two datasets of Chinese articles. Our approach achieves similar performance in comparison to SVM. Moreover, our rule-based approach is much more portable and adaptable to various topic domains since it does not require the manual annotation of large amounts of training data. These results illustrate the effectiveness of our proposed method and its advantages against learning-based approaches.
(1)提出了基于综合价值具有增量特性的主题爬虫。在主题相关信息采集方面,以往的爬虫在满足爬全率(recall)的同时牲牺了爬准率(harvest)以及爬行效率,而为了提高爬准率往往又降低了爬全率。通过采用前后端分类器,前端基于链接语境图训练链接预测分类器,使爬虫具有一定的穿越隧道的能力,后端使用主题内容分类器识别主题相关网页,同时使用网页内容可视化分块技术,并基于链接的综合价值进行网页预测,提高了爬全率、爬准率及爬行效率。
(2)提出了基于无监督聚类的PU文本分类方法。传统的机器学习文本分类模型需要大量的标注语料做为训练集,PU文本分类算法是解决某些机器学习中训练样本获取代价过大,尤其是反例样本较难获取的实际问题,而传统的分类算法大都需要正例和反例数据集才能取得良好的效果,因此要使用传统的分类方法来解决面向PU的分类问题,U集中可信反例的提取是分类器能够取得良好效果的关键,本文提出了有效的可信反例提取算法(基于聚类的可信反例提取算法)-CBRN,并对已有的PU文本分类算法进行了改进并提出了SPY-SVM算法,提高了可信反例提取的数量和准确率,也提高了PU文本分类的准确率。
(3)评价挖掘是针对特定领域主题的主观性文本自动提取有用的情感信息和知识,可为政府部门、企业及用户提供有价值的意见信息。本文针对中文文本进行褒、贬情感倾向性分析,提出了三种情感倾向性分析算法,1)基于规则及情感词提取评价四元组的评价挖掘算法和基于unigram+评价短语特征的机器学习评价挖掘算法,2)基于字符串核函数的评价挖掘算法,3)基于规则及聚合模型的句子级到篇章级的中文评价挖掘算法。
With the rapid development of Internet technologies, information in web mounts up exponentially. In the meanwhile, interaction technologies of web2.0 enable people to communicate on the Internet and post variety opinions and comments. There has been a variety of public sentiment information on the Internet. Therefore people are facing great difficulties in searching for desired information because the information is always hidden in information ocean. How to get public sentiment information about domain-specific events? The combination of focused crawler technology and sentiment analysis technology make it possible to resolve this problem. By analyzing public sentiment information in specific domain can support decision making of policy-making departments, help enterprises improve program plans, and provide users with useful information. To meet these demands, this dissertation proposes a lot of key techniques, theories and methods as shown in the following three sections:
1. The dissertation proposes Focus Crawler with incremental capability based on synthetic estimate value. Subjects on the web are distributed interweavedly, but the same subject on web has certain distribution rules. We summarize these rules as Hub, Sibling/Linkage Locality, Site subject, Tunnel, Topic trap. We design Focus Crawlers based on the proposed rules. Recent years have witnessed a lot of research on focus crawlers. However, these studies have some limitations. They improved recall at the cost of sacrificing harvest and efficiency. On the other hand, recall would decrease if harvest were satisfied. In this dissertation, we propose front-end/back-end classifiers as the part of link's topic-relevance forecasting. The front-end classifier trains classification model based on linkage context graph, uses the webpage visualized content block partition technique, and predicts whether the link of webpage is topic-relevance based on link's synthetic values. It endows focus crawlers with the ability of going through tunnel, i.e., enables focused crawler to start from some topic-relevant webpage, pass through some irrelevant webpage and reach other topic-relevant webpage. The back-end classifier is used to recognize topic-relevant WebPages based on text content of WebPages. The experimental results show that our focused crawler can dramatically improve recall rate, harvest rate and efficiency.
2. The PU-Oriented Text Classifier Based on Unsupervised Clustered Learning Algorithm is proposed. Traditional text classification models are based on machine learning and need a large amount of labeled corpus as train datasets. So a large number of labeled training documents/webpages (often negative training data) are needed to build accurate classifiers. In text classification, the labeling is typically done manually by reading the documents/webpages, which is a labor-intensive and time-consuming process. Collecting negative training examples is especially painstaking and tedious because (1) negative training examples must uniformly represent the universal set except the positive class (e.g., sample of a nonhomepage should represent the Internet uniformly excluding the homepages), and (2) manually collecting negative training examples tends to cause unconscious bias because of human's unintentional prejudice, which could deteriorate classification performance such as accuracy, precision, recall, etc. PU-Oriented text classifier aims to solve the problems in machine learning that no labeled negative documents are available in the training example set or negative examples are very difficult to collect. Traditional classification algorithm cannot obtain good performance without sufficient positive and negative training dataset. When using traditional classifier to conduct PU-oriented text classification, the key is the extraction of reliable negative training example from unlabeled documents/webpages. The PU-oriented text classification based on machine learning often adopts a two-step approach by making use of both positive and unlabeled examples. At the first step, a lot of reliable negative documents are identified. At the second step, the classifiers are constructed iterative based on training datasets. In this dissertation, the clustering based reliable negative example extraction (CBRN) algorithm is proposed. The number and the accuracy of reliable negative examples extraction is improved. Existing classification is improved, which builds a set of classifiers by iterative applying the SPY-SVM algorithm. This approach randomly selects s% of the documents from the positive set P as the spies and add them into unlabeled datasets. These spies can help improve the accuracy of identifying the negative from unlabeled datasets, and train the classifier iterative until termination condition meets. Experimental results show that our method outperforms other algorithms in terms of accuracy, recall, precision and Fl-measure.
3. Opinion mining or sentiment analyzer is to extract sentiment (or opinion) about a subject from online subjective text documents. At first it classifies the sentiment of an entire document about a subject. It can provide valuable information for government, enterprise and users. The dissertation proposes three semantic orientation analysis (positive, negative and neutral semantic orientation analysis) algorithms for Chinese text. These three methods are described as below:
1) Polarity Classification of Public Health Opinions in Chinese text. With frequently bursting of public health events over the world, people are increasingly expressing their views on these events online. Government agencies need to response and make policies according to these views. We study Chinese opinion mining under the context of public health. This dissertation proposes two complementary approaches-a sentiment word based approach and a machine learning approach. The Chinese sentiment word based approach extracts an opinion quadruple from each single sentence based on rules. We notice that different types of sentences have different contributions to the overall polarity and take into account three types of sentences:common sentences, first-person sentences, topical sentences. We give different weights to these three types of sentences when synthesizing the overall polarity scores of entire review through weighted average. The machine learning based approach extracts unigrams and opinion phrase features by labeling train datasets, selects features by information gain method and train sentiment classification model using ten-fold cross validation. The experiment results show that both methods achieve good performance.
2) This dissertation proposes a string kernel based approach for sentiment classification on Chinese reviews. Machine learning based sentiment classification approaches depend on a feature vector which represents a text. They usually utilize words or n-grams as features and construct feature vectors according to their presence/absence or frequencies. They use these feature vectors to construct sentiment classification model. The selection of feature set is considered as the most important point in classifying documents. The features extract module not only needs comprehensive experts'knowledge, but also ignores the information on word positions, i.e., may lost important information when extracting features such as the position of words and mutual information between words. The word order is extremely important to sentiment analysis. This dissertation proposes sentiment classification for Chinese reviews using machine learning methods based on string kernel. The features are all possible ordered subsequences of characters. It can construct sentiment classification model if important information are not lost. We conduct experiments to show the power of our approach as well.
3) Sentiment analysis of Chinese documents from sentence to document level:This dissertation proposes a rule-based approach including two phases:first, determining each sentence's sentiment based on word dependency and context modifier component, second, aggregating sentences polarity scores to predict the document sentiment. We assign sentences with different weights to adjust their contribution to the overall polarity based on five features, including position of the sentence, weight term/tf-isf weighted of the sentence, the similarity between the sentence and the headline, the occurrence of keywords in the sentence, and the first-person mode. We report the experimental results of comparing our approaches with three machine learning-based approaches based on two datasets of Chinese articles. Our approach achieves similar performance in comparison to SVM. Moreover, our rule-based approach is much more portable and adaptable to various topic domains since it does not require the manual annotation of large amounts of training data. These results illustrate the effectiveness of our proposed method and its advantages against learning-based approaches.
引文
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