Summarizing Threads in Blogs Using Opinion Polarity (original) (raw)

Summarizing opinions in blog threads

2009

In this paper we present an approach to summarizing positive and negative opinions in blog threads. We first run a sentiment analysis system and consequently pass its output through a standard LSA-based text summarization system. Further on, we evaluate our approach and present the results obtained, which we believe are promising in the context of multi-document text summarization. Finally, we discuss the main issues in applying standard text summarization techniques to the slightly different task of summarizing opinions in blog threads.

Classification based approach for Summarizing Opinions in Blog Posts

2009

With the growth of web, people are using it as a medium for expressing their opinions, thoughts through blog posts, reviews (in the form of ratings), and forums. Blogosphere is a place where people read, write their views and make comments on others views or thoughts there by exchanging information. It will be very difficult for any business, organization or individual to go through and understand thoughts expressed by others on a product or topic which they are interested in. Hence a summarization system which extracts, analyze and summarize opinions will be useful. Our Summarization system exactly does the same for blog posts. The entire process of summary generation is done in three stages, extract sentences which are sources for opinion (Opinion Mining), then analyze the extracted opinions to determine polarity (Opinion Analysis) and finally ranking the opinion sentences (Opinion Summarization). We present a classification based approach for extracting and analyzing opinions and how we use this approach to rank sentences for generating summary.

Challenges and solutions in the opinion summarization of user-generated content

Journal of Intelligent Information Systems, 2012

The present is marked by the influence of the Social Web on societies and people worldwide. In this context, users generate large amounts of data, especially containing opinion, which has been proven useful for many real-world applications. In order to extract knowledge from user-generated content, automatic methods must be developed. In this paper, we present different approaches to multi-document summarization of opinion from blogs and reviews. We apply these approaches to: (a) identify positive and negative opinions in blog threads in order to produce a list of arguments in favor and against a given topic and (b) summarize the opinion expressed in reviews. Subsequently, we evaluate the proposed methods on two distinct datasets and analyze the quality of the obtained results, as well as discuss the errors produced. Although much remains to be done, the approaches we propose obtain encouraging results and point to clear directions in which further improvements can be made.

Opinion extraction, summarization and tracking in news and blog corpora

… of AAAI-2006 Spring Symposium on …, 2006

Humans like to express their opinions and are eager to know others' opinions. Automatically mining and organizing opinions from heterogeneous information sources are very useful for individuals, organizations and even governments. Opinion extraction, opinion summarization and opinion tracking are three important techniques for understanding opinions. Opinion extraction mines opinions at word, sentence and document levels from articles. Opinion summarization summarizes opinions of articles by telling sentiment polarities, degree and the correlated events. In this paper, both news and web blog articles are investigated. TREC, NTCIR and articles collected from web blogs serve as the information sources for opinion extraction. Documents related to the issue of animal cloning are selected as the experimental materials. Algorithms for opinion extraction at word, sentence and document level are proposed. The issue of relevant sentence selection is discussed, and then topical and opinionated information are summarized. Opinion summarizations are visualized by representative sentences. Text-based summaries in different languages, and from different sources, are compared. Finally, an opinionated curve showing supportive and nonsupportive degree along the timeline is illustrated by an opinion tracking system.

Effective and efficient polarity estimation in blogs based on sentence-level evidence

2011

One of the core tasks in Opinion Mining consists of esti-mating the polarity of the opinionated documents found. In some scenarios (e.g. blogs), this estimation is severely af-fected by sentences that are off-topic or that simply do not express any opinion. In fact, the key sentiments in a blog post often appear in specific locations of the text. In this paper we propose several effective and robust polarity de-tection methods based on different sentence features. We show that we can successfully determine the polarity of doc-uments guided by a sentence-level analysis that takes into account topicality and the location in the blog post of the subjective sentences. Our experimental results show that some of our proposed variants are both highly effective and computationally-lightweight.

An effective statistical approach to blog post opinion retrieval

2008

Abstract Finding opinionated blog posts is still an open problem in information retrieval, as exemplified by the recent TREC blog tracks. Most of the current solutions involve the use of external resources and manual efforts in identifying subjective features. In this paper, we propose a novel and effective dictionary-based statistical approach, which automatically derives evidence for subjectivity from the blog collection itself, without requiring any manual effort.

A linguistic approach for opinionated documents summary

Future Computing and Informatics Journal, 2017

Now, the web pages contain opinions on almost anything, at review sites, forums, discussion groups, and blogs which called user generated content. They contain valuable information for different users such as persons or organizations, the processes of collecting, analyzing and classifying them to positive or negative opinions in addition to summarizing the opinions are considered a very important research issue. Summarizing opinions helps users to explore the opinion of others about the key aspects of a topic or an entity. The proposed opinion summarization system receives a document that contains sentences expressing opinions about an entity and generates a summary considering the important aspects, their relations, their sentiments and the textual evidences, as expressed in the reviews. In this paper we present a linguistic approach to summarize the opinionated documents across different domains, our evaluation based on a dataset of hotels, cars and various products reviews. The reviews collected from Tripadvisor, Amazon and Edmunds, each review document consist of a set of unordered, redundant reviews sentence, there are approximately 100 sentences per review document. The summary depends on the type of the opinion which is direct, comparative, or superlative. Each type is assigned to a specialist who is responsible for the summary.

IIT Kharagpur at TAC 2008: Statistical Model for Opinion Summarization

nist.gov

The TAC 2008 Opinion Summarization task was to generate query-based summary of opinions as present in the given collection of blog documents. One of the largest sources of information on the web is in the form of weblogs, where people express their opinions on a variety of ...

Towards a unified framework for opinion retrieval, mining and summarization

Journal of Intelligent Information Systems, 2012

The exponential increase of subjective, user-generated content since the birth of the Social Web, has led to the necessity of developing automatic text processing systems able to extract, process and present relevant knowledge. In this paper, we tackle the Opinion Retrieval, Mining and Summarization task, by proposing a unified framework, composed of three crucial components (information retrieval, opinion mining and text summarization) that allow the retrieval, classification and summarization of subjective information. An extensive analysis is conducted, where different configurations of the framework are suggested and analyzed, in order to determine which is the best one, and under which conditions. The evaluation carried out and the results obtained show the appropriateness of the individual components, as well as the framework as a whole. By achieving an improvement over 10% compared to the state-of-the-art approaches in the context of blogs, we can conclude that subjective text can be efficiently dealt with by means of our proposed framework.

Positive, Negative, or Mixed? Mining Blogs for Opinions

2009

The rich non-factual information on the blogosphere presents interesting research questions. In this paper, we present a study on analysis of blog posts for their sentiment by using a generic sentiment lexicon. In particular, we applied Support Vector Machine to classify blog posts into three categories of opinions: positive, negative and mixed. We investigated the performance difference between global topic-independent and local topic-dependent opinion classification on a collection of blogs. Our experiment shows that topic-dependent classification performs significantly better than topic-independent classification, and this result indicates high interaction between sentiment words and topic.