毕业设计(论文)外文翻译
外文文献：Opinion Mining and Summarization of Reviews in Web Forums
Opinion Mining and Summarization of Reviews in Web Forums
ABSTRACT
The Internet has made life of every individual (web users) very simple and sophisticated. In recent years people use the web for many reasons like personal communication, entertainment, online shopping, general search and so on. Internet forums also act as a medium of exchange for sharing resources and knowledge. Though commercial review websites allow users to express their opinions in whatever way they feel, number of reviews for specific product available is enormous. Hence it becomes difficult for the customers to read all the reviews to make a decision. In this paper we propose an extraction technique to score the reviews and summarize the opinions to end user. Based on opinions mined it is decided as whether to recommend the product to the user or not. This paper mainly focuses on providing a methodology for mining the opinions using generic user focused reviews. The experiments performed were quite promising for the data set used.
Categories and Subject Descriptors
H.2.8 [Database Management]: Database Applications – data mining. I.2.7 [Artificial Intelligence]: Natural Language Processing – text analysis
General Terms
Algorithms, Experimentation, Human Factors
Keywords
Opinion Mining, Product Reviews, Sentiment Analysis, Summarization, Recommendation.
1.INTRODUCTION
The user generated content on the web such as personal experiences and opinions about a product, a movie or any other thing in the form of reviews play a very important role in business, education, e-commerce, etc. Online review websites like Amazona, IMDBb, Epinionsc, Cnetd, eBaye allow users to express their opinions for the information they are interested in. So there is a huge amount of information available in online documents such as blogs, newsgroup postings, discussion forums and web pages that are useful to both customers and manufacturers. Customers could assess a product by reading opinions of other customers, which will help them to decide whether to purchase the product or not. On the other hand, the manufacturers can improve their products by tracking the feedback from the customers. With the emergence of e-commerce, the number of reviews that a product receives is quite high and many reviews are lengthy with only few sentences containing the actual opinions on the product. It is not easy for a customer to read all the reviews in order to decide on whether to buy the product or not. If the customer reads only a few reviews, then the opinion might be biased. Manufacturers also find it difficult to analyze all the reviews and to interpret the customer opinions. So an opinion miner processing the reviews would solve this issue in identifying whether a piece of text conveys a positive or negative opinion about a particular product.
Since we are in the Internet era and due to the rapid growth of online contents, advancement in the development of products had opened the issue worldwide among educationalists, especially among research community. Few researchers have focused on opinion mining and summarization using lexical information in terms of word-class associations [1], rule-based classification combined with supervised and machine learning approach [2], HMM-based learning methods [3]. Most of the existing methods in opinion mining try to mine and extract the overall sentiment revealed in a document, either positive or negative, or somewhere in between(i.e. neutral review). The format of the reviews in the online review sites like Amazon, Epinions and Cnet differ from one another. However our paper focuses on processing reviews in shorter forms or free forms that run up to several lines. We stick on to extracting the reviews in form of summary only. We propose a method to score the reviews based on the opinion words and provide recommendation as to whether to accept or reject the product. Reviews are scored based on the summarization algorithm (discussed in section 3.2.4).
The scoring and evaluation of the sentences in the example shown in the section 3.2.4, is a representation of the real-world scenario in web-forums and the method is expected to behave in a very similar manner when put to test with a large real-world data set.
The paper is structured as follows: Section 2 describes the related work. Section 3 presents the architectural diagram, the techniques proposed and the results. Section 4 provides the conclusion and future work. Related references carried out for the task are given in Section 6
2.RELATED WORKS
Minqing Hu and Bing Liu [4] developed a feature-based opinion summarization system that mines and summarizes all the customer reviews of a product. This system differs from other text summarization systems in number of ways. It mines only the features of the product that customers have expressed their opinions on and the summary generated is structured rather than a free text document as generated by other text summarization systems. The summarization is performed by the system in three steps: (1) Finding the features on which the customers have expressed their opinions. (2) Identifying opinion sentences in each review and analyzing the polarity of the sentence (3) Producing a summary. The main drawbacks of t
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毕业设计(论文)外文翻译
学生姓名： 凌云菲 学 号： 1401160303
所在学院： 计算机科学与技术学院
专 业： 计算机科学与技术
设计(论文)题目： 基于Maven的个人博客系统
指导教师： 刘学军
2020年2月 22日
外文文献：Opinion Mining and Summarization of Reviews in Web Forums
Opinion Mining and Summarization of Reviews in Web Forums
ABSTRACT
The Internet has made life of every individual (web users) very simple and sophisticated. In recent years people use the web for many reasons like personal communication, entertainment, online shopping, general search and so on. Internet forums also act as a medium of exchange for sharing resources and knowledge. Though commercial review websites allow users to express their opinions in whatever way they feel, number of reviews for specific product available is enormous. Hence it becomes difficult for the customers to read all the reviews to make a decision. In this paper we propose an extraction technique to score the reviews and summarize the opinions to end user. Based on opinions mined it is decided as whether to recommend the product to the user or not. This paper mainly focuses on providing a methodology for mining the opinions using generic user focused reviews. The experiments performed were quite promising for the data set used.
Categories and Subject Descriptors
H.2.8 [Database Management]: Database Applications – data mining. I.2.7 [Artificial Intelligence]: Natural Language Processing – text analysis
General Terms
Algorithms, Experimentation, Human Factors
Keywords
Opinion Mining, Product Reviews, Sentiment Analysis, Summarization, Recommendation.
1.INTRODUCTION
The user generated content on the web such as personal experiences and opinions about a product, a movie or any other thing in the form of reviews play a very important role in business, education, e-commerce, etc. Online review websites like Amazona, IMDBb, Epinionsc, Cnetd, eBaye allow users to express their opinions for the information they are interested in. So there is a huge amount of information available in online documents such as blogs, newsgroup postings, discussion forums and web pages that are useful to both customers and manufacturers. Customers could assess a product by reading opinions of other customers, which will help them to decide whether to purchase the product or not. On the other hand, the manufacturers can improve their products by tracking the feedback from the customers. With the emergence of e-commerce, the number of reviews that a product receives is quite high and many reviews are lengthy with only few sentences containing the actual opinions on the product. It is not easy for a customer to read all the reviews in order to decide on whether to buy the product or not. If the customer reads only a few reviews, then the opinion might be biased. Manufacturers also find it difficult to analyze all the reviews and to interpret the customer opinions. So an opinion miner processing the reviews would solve this issue in identifying whether a piece of text conveys a positive or negative opinion about a particular product.
Since we are in the Internet era and due to the rapid growth of online contents, advancement in the development of products had opened the issue worldwide among educationalists, especially among research community. Few researchers have focused on opinion mining and summarization using lexical information in terms of word-class associations [1], rule-based classification combined with supervised and machine learning approach [2], HMM-based learning methods [3]. Most of the existing methods in opinion mining try to mine and extract the overall sentiment revealed in a document, either positive or negative, or somewhere in between(i.e. neutral review). The format of the reviews in the online review sites like Amazon, Epinions and Cnet differ from one another. However our paper focuses on processing reviews in shorter forms or free forms that run up to several lines. We stick on to extracting the reviews in form of summary only. We propose a method to score the reviews based on the opinion words and provide recommendation as to whether to accept or reject the product. Reviews are scored based on the summarization algorithm (discussed in section 3.2.4).
The scoring and evaluation of the sentences in the example shown in the section 3.2.4, is a representation of the real-world scenario in web-forums and the method is expected to behave in a very similar manner when put to test with a large real-world data set.
The paper is structured as follows: Section 2 describes the related work. Section 3 presents the architectural diagram, the techniques proposed and the results. Section 4 provides the conclusion and future work. Related references carried out for the task are given in Section 6
2.RELATED WORKS
Minqing Hu and Bing Liu [4] developed a feature-based opinion summarization system that mines and summarizes all the customer reviews of a product. This system differs from other text summarization systems in number of ways. It mines only the features of the product that customers have expressed their opinions on and the summary generated is structured rather than a free text document as generated by other text summarization systems. The summarization is performed by the system in three steps: (1) Finding the features on which the customers have expressed their opinions. (2) Identifying opinion sentences in each review and analyzing the polarity of the sentence (3) Producing a summary. The main drawbacks of t
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