Fusion Approach to Finding Opinions in Blogosphere (original) (raw)

Integrating proximity to subjective sentences for blog opinion retrieval

2009

Abstract. Opinion finding is a challenging retrieval task, where it has been shown that it is especially difficult to improve over a strongly performing topic-relevance baseline. In this paper, we propose a novel approach for opinion finding, which takes into account the proximity of query terms to subjective sentences in a document. We adapt two stateof-the-art opinion detection techniques to identify subjective sentences from the retrieved documents.

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.

Finding Opinionated Blogs Using Statistical Classifiers and Lexical Features

Third International AAAI Conference on Weblogs and …, 2009

This paper systematically exploited various lexical features for opinion analysis on blog data using a statistical learning framework. Our experimental results using the TREC Blog track data show that all the features we explored effectively represent opinion expressions, and different classification strategies have a significant impact on opinion classification performance. We also present results when combining opinion analysis with the retrieval component for the task of retrieving relevant and opinionated blogs. Compared with the best results in the TREC evaluation, our system achieves reasonable performance, but does not rely on much human knowledge or deep level linguistic analysis.

Investigating learning approaches for blog post opinion retrieval

2009

Abstract. Blog post opinion retrieval is the problem of identifying posts which express an opinion about a particular topic. Usually the problem is solved using a 3 step process in which relevant posts are first retrieved, then opinion scores are generated for each document, and finally the opinion and relevance scores are combined to produce a single ranking. In this paper, we study the effectiveness of classification and rank learning techniques for solving the blog post opinion retrieval problem.

Blog mining through opinionated words

2006

Intent mining is a special kind of document analysis whose goal is to assess the attitude of the document author with respect to a given subject. Opinion mining is a kind of intent mining where the attitude is a positive or negative opinion. Most systems tackle the problem with a two step approach, an information retrieval followed by a postprocess or filter phase to identify opinionated blogs. We explored a single stage approach to opinion mining, retrieving opinionated documents ranked with a special ranking function which exploits an index enriched with opinion tags. A set of subjective words are used as tags for identifying opinionated sentences. Subjective words are marked as "opinionated" and are used in the retrieval phase to boost the rank of documents containing them. In indexing the collection, we recovered the relevant content from the blog permalink pages, exploiting HTML metadata about the generator and heuristics to remove irrelevant parts from the body. The index also contains information about the occurrence of opinionated words, extracted from an analysis of WordNet glosses. The experiments compared the precision of normal queries with respect to queries which included as constraint the proximity to an opinionated word. The results show a significant improvement in precision for both topic relevance and opinion relevance.

The BlogVox Opinion Retrieval System

2006

The BlogVox system retrieves opinionated blog posts specified by ad hoc queries. BlogVox was developed for the 2006 TREC blog track by the University of Maryland, Baltimore County and the Johns Hopkins University Applied Physics Laboratory using a novel system to recognize legitimate posts and discriminate against spam blogs. It also processes posts to eliminate extraneous non-content, including blog-rolls, link-rolls, advertisements and sidebars. After retrieving posts relevant to a topic query, the system processes them to produce a set of independent features estimating the likelihood that a post expresses an opinion about the topic. These are combined using an SVM-based system and integrated with the relevancy score to rank the results. We evaluate BlogVox's performance against human assessors. We also evaluate the individual splog filtering and non-content removal components of BlogVox.

Fdu at trec 2007: opinion retrieval of blog track

2007

This paper describes our participation in the opinion retrieval task at Blog Track 07. The system consisted of the preprocess part, the topic retrieval part and sentiment analysis part. In the topic retrieval part, we adopted pseudo-relevance feedback and a novel approach to form a modified query. In the sentiment analysis part, each blog post was given an opinion score based on the sentences contained in this post. The subjectivity of each sentence was predicted by a CME classifier. Then the blog posts were reranked based on the similarity given by the topic retrieval and the opinion score given by the sentiment analysis.

Mining opinions from the Web: Beyond relevance retrieval

Journal of the American Society for Information Science and Technology, 2007

Documents discussing public affairs, common themes, interesting products, and so on, are reported and distributed on the Web. Positive and negative opinions embedded in documents are useful references and feedbacks for governments to improve their services, for companies to market their products, and for customers to purchase their objects. Web opinion mining aims to extract, summarize, and track various aspects of subjective information on the Web. Mining subjective information enables traditional information retrieval (IR) systems to retrieve more data from human viewpoints and provide information with finer granularity. Opinion extraction identifies opinion holders, extracts the relevant opinion sentences, and decides their polarities. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the nonsupportive evidence. Opinion tracking captures subjective information from various genres and monitors the developments of opinions from spatial and temporal dimensions. To demonstrate and evaluate the proposed opinion mining algorithms, news and bloggers' articles are adopted. Documents in the evaluation corpora are tagged in different granularities from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f-measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve f-measure 62.16% at the sentence level and 74.37% at the document level.

Web opinion mining: how to extract opinions from blogs

2008

The growing popularity of Web 2.0 provides with increasing numbers of documents expressing opinions on different topics. Recently, new research approaches have been defined in order to automatically extract such opinions from the Internet. They usually consider opinions to be expressed through adjectives, and make extensive use of either general dictionaries or experts to provide the relevant adjectives. Unfortunately, these approaches suffer from the following drawback: in a specific domain, a given adjective may either not exist or have a different meaning from another domain. In this paper, we propose a new approach focusing on two steps. First, we automatically extract a learning dataset for a specific domain from the Internet. Secondly, from this learning set we extract the set of positive and negative adjectives relevant to the domain. The usefulness of our approach was demonstrated by experiments performed on real data.