Search (3805 results, page 1 of 191)

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Abstract

We present CopeOpi, an opinion-analysis system, which extracts from the Web opinions about specific targets, summarizes the polarity and strength of these opinions, and tracks opinion variations over time. Objects that yield similar opinion tendencies over a certain time period may be correlated due to the latent causal events. CopeOpi discovers relationships among objects based on their opinion-tracking plots and collocations. Event bursts are detected from the tracking plots, and the strength of opinion relationships is determined by the coverage of these plots. To evaluate opinion mining, we use the NTCIR corpus annotated with opinion information at sentence and document levels. CopeOpi achieves sentence- and document-level f-measures of 62% and 74%. For relationship discovery, we collected 1.3M economics-related documents from 93 Web sources over 22 months, and analyzed collocation-based, opinion-based, and hybrid models. We consider as correlated company pairs that demonstrate similar stock-price variations, and selected these as the gold standard for evaluation. Results show that opinion-based and collocation-based models complement each other, and that integrated models perform the best. The top 25, 50, and 100 pairs discovered achieve precision rates of 1, 0.92, and 0.79, respectively.

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Date

22. 3.2013 19:53:52

Series

Opinion paper

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Abstract

I analyze the text of an article that appeared in this journal in 2007 that published the results of a questionnaire in which a number of experts were asked to define the concepts of data, information, and knowledge. I apply standard information retrieval techniques to build a list of the most frequent terms in each set of definitions. I then apply information extraction techniques to analyze how the top terms are used in the definitions. As a result, I draw data-driven conclusions about the aggregate opinion of the experts. I contrast this with the original analysis of the data to provide readers with an alternative viewpoint on what the data tell us.

Date

16. 6.2014 19:22:57

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Date

15. 5.2019 19:22:59

Series

Opinion paper

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Abstract

Opinion mining refers to the use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information in textual material. Opinion mining, also known as sentiment analysis, has received a lot of attention in recent times, as it provides a number of tools to analyze public opinion on a number of different topics. Comparative opinion mining is a subfield of opinion mining which deals with identifying and extracting information that is expressed in a comparative form (e.g., "paper X is better than the Y"). Comparative opinion mining plays a very important role when one tries to evaluate something because it provides a reference point for the comparison. This paper provides a review of the area of comparative opinion mining. It is the first review that cover specifically this topic as all previous reviews dealt mostly with general opinion mining. This survey covers comparative opinion mining from two different angles. One from the perspective of techniques and the other from the perspective of comparative opinion elements. It also incorporates preprocessing tools as well as data set that were used by past researchers that can be useful to future researchers in the field of comparative opinion mining.

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Abstract

Purpose - The purpose of this paper is to discuss sentiment search, which not only retrieves data related to submitted keywords but also identifies sentiment opinion implied in the retrieved data and the subject targeted by this opinion. Design/methodology/approach - The authors propose a retrieval framework known as Cross-Domain Sentiment Search (CSS), which combines the usage of domain ontologies with specific linguistic rules to handle sentiment terms in textual data. The CSS framework also supports incrementally enriching domain ontologies when applied in new domains. Findings - The authors found that domain ontologies are extremely helpful when CSS is applied in specific domains. In the meantime, the embedded linguistic rules make CSS achieve better performance as compared to data mining techniques. Research limitations/implications - The approach has been initially applied in a real social monitoring system of a professional IT company. Thus, it is proved to be able to handle real data acquired from social media channels such as electronic newspapers or social networks. Originality/value - The authors have placed aspect-based sentiment analysis in the context of semantic search and introduced the CSS framework for the whole sentiment search process. The formal definitions of Sentiment Ontology and aspect-based sentiment analysis are also presented. This distinguishes the work from other related works.

Date

20. 1.2015 18:30:22

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Abstract

Political sentiment analysis using social media, especially Twitter, has attracted wide interest in recent years. In such research, opinions about politicians are typically divided into positive, negative, or neutral. In our research, the goal is to mine political opinion from social media at a higher resolution by assessing statements of opinion related to the personality traits of politicians; this is an angle that has not yet been considered in social media research. A second goal is to contribute a novel retrieval-based approach for tracking public perception of personality using Gough and Heilbrun's Adjective Check List (ACL) of 110 terms describing key traits. This is in contrast to the typical lexical and machine-learning approaches used in sentiment analysis. High-precision search templates developed from the ACL were run on an 18-month span of Twitter posts mentioning Obama and Romney and these retrieved more than half a million tweets. For example, the results indicated that Romney was perceived as more of an achiever and Obama was perceived as somewhat more friendly. The traits were also aggregated into 14 broad personality dimensions. For example, Obama rated far higher than Romney on the Moderation dimension and lower on the Machiavellianism dimension. The temporal variability of such perceptions was explored.

Date

22. 1.2016 11:25:47

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Abstract

Opinion retrieval is the task of finding documents that express an opinion about a given query. A key challenge in opinion retrieval is to capture the query-related opinion score of a document. Existing methods rely mainly on the proximity information between the opinion terms and the query terms to address the key challenge. In this study, we propose to incorporate the syntactic and semantic information of terms into a probabilistic model to capture the query-related opinion score more accurately. The syntactic tree structure of a sentence is used to evaluate the modifying probability between an opinion term and a noun within the sentence with a tree kernel method. Moreover, WordNet and the probabilistic topic model are used to evaluate the semantic relatedness between any noun and the given query. The experimental results over standard TREC baselines on the benchmark BLOG06 collection demonstrate the effectiveness of our proposed method, in comparison with the proximity-based method and other baselines.

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Content

Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.

Date

8. 1.2013 10:22:32

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Abstract

Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic-level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1-measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo.

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Abstract

Opinion mining is one of the most important research tasks in the information retrieval research community. With the huge volume of opinionated data available on the Web, approaches must be developed to differentiate opinion from fact. In this paper, we present a lexicon-based approach for opinion retrieval. Generally, opinion retrieval consists of two stages: relevance to the query and opinion detection. In our work, we focus on the second state which itself focusses on detecting opinionated documents . We compare the document to be analyzed with opinionated sources that contain subjective information. We hypothesize that a document with a strong similarity to opinionated sources is more likely to be opinionated itself. Typical lexicon-based approaches treat and choose their opinion sources according to their test collection, then calculate the opinion score based on the frequency of subjective terms in the document. In our work, we use different open opinion collections without any specific treatment and consider them as a reference collection. We then use language models to determine opinion scores. The analysis document and reference collection are represented by different language models (i.e., Dirichlet, Jelinek-Mercer and two-stage models). These language models are generally used in information retrieval to represent the relationship between documents and queries. However, in our study, we modify these language models to represent opinionated documents. We carry out several experiments using Text REtrieval Conference (TREC) Blogs 06 as our analysis collection and Internet Movie Data Bases (IMDB), Multi-Perspective Question Answering (MPQA) and CHESLY as our reference collection. To improve opinion detection, we study the impact of using different language models to represent the document and reference collection alongside different combinations of opinion and retrieval scores. We then use this data to deduce the best opinion detection models. Using the best models, our approach improves on the best baseline of TREC Blog (baseline4) by 30%.

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Content

1. Agrarwissenschaften 1981. - 3. Allgemeine Geographie 2.1972. - 3a. Allgemeine Naturwissenschaften 1.1973. - 4. Allgemeine Sprachwissenschaft, Allgemeine Literaturwissenschaft 2.1971. - 6. Allgemeines. 5.1983. - 7. Anglistik 3.1976. - 8. Astronomie, Geodäsie 4.1977. - 12. bio Biologie, bcp Biochemie-Biophysik, bot Botanik, zoo Zoologie 1981. - 13. Bremensien 3.1983. - 13a. Buch- und Bibliothekswesen 3.1975. - 14. Chemie 4.1977. - 14a. Elektrotechnik 1974. - 15 Ethnologie 2.1976. - 16,1. Geowissenschaften. Sachteil 3.1977. - 16,2. Geowissenschaften. Regionaler Teil 3.1977. - 17. Germanistik 6.1984. - 17a,1. Geschichte. Teilsystematik hil. - 17a,2. Geschichte. Teilsystematik his Neuere Geschichte. - 17a,3. Geschichte. Teilsystematik hit Neueste Geschichte. - 18. Humanbiologie 2.1983. - 19. Ingenieurwissenschaften 1974. - 20. siehe 14a. - 21. klassische Philologie 3.1977. - 22. Klinische Medizin 1975. - 23. Kunstgeschichte 2.1971. - 24. Kybernetik. 2.1975. - 25. Mathematik 3.1974. - 26. Medizin 1976. - 26a. Militärwissenschaft 1985. - 27. Musikwissenschaft 1978. - 27a. Noten 2.1974. - 28. Ozeanographie 3.1977. -29. Pädagogik 8.1985. - 30. Philosphie 3.1974. - 31. Physik 3.1974. - 33. Politik, Politische Wissenschaft, Sozialwissenschaft. Soziologie. Länderschlüssel. Register 1981. - 34. Psychologie 2.1972. - 35. Publizistik und Kommunikationswissenschaft 1985. - 36. Rechtswissenschaften 1986. - 37. Regionale Geograpgie 3.1975. - 37a. Religionswissenschaft 1970. - 38. Romanistik 3.1976. - 39. Skandinavistik 4.1985. - 40. Slavistik 1977. - 40a. Sonstige Sprachen und Literaturen 1973. - 43. Sport 4.1983. - 44. Theaterwissenschaft 1985. - 45. Theologie 2.1976. - 45a. Ur- und Frühgeschichte, Archäologie 1970. - 47. Volkskunde 1976. - 47a. Wirtschaftswissenschaften 1971 // Schlüssel: 1. Länderschlüssel 1971. - 2. Formenschlüssel (Kurzform) 1974. - 3. Personenschlüssel Literatur 5. Fassung 1968

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Source

Public opinion quarterly. 34(1970), S.159-170

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Abstract

Opinion words are crucial information for sentiment analysis. In some text, however, opinion words are absent or highly ambiguous. The resulting implicit opinions are more difficult to extract and label than explicit ones. In this paper, cutting-edge machine-learning approaches - deep neural network and word-embedding - are adopted for implicit opinion mining at the snippet and clause levels. Hotel reviews written in Chinese are collected and annotated as the experimental data set. Results show the convolutional neural network models not only outperform traditional support vector machine models, but also capture hidden knowledge within the raw text. The strength of word-embedding is also analyzed.

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Abstract

Proposes 2 theories to explain the development and structural organization of public opinion. They integrate dynamic social impact theory (DSIT), work on interattitudinal structure, and parallel constraint satisfaction processes in connectionist models of cognition. Sketches a dynamic theory of cognitive impact, and reviews DSIT. Presents an integrated cognitive social model of the processes through which public opinion develops and dynamically changes over time

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Abstract

We examine how opinion distributions (i.e., opinion polarization and convergence over time) differ across product salient platforms (product platforms) versus product non-salient platforms (non-product platforms). Drawing on the theory of impression management, we hypothesize and explain when and why consumers choose to post their comments on different platforms, and how their behavior will be affected when they choose to post on online platforms. To test the hypotheses, we collected and text-mined online posts from product platforms such as review aggregator sites, discussion forums, and consumer rating websites, and non-product platforms such as microblogs. The results showed that product platforms have more polarized opinions, and exhibit more convergence in opinion across time, compared with non-product platforms. Our findings advise researchers and practitioners to pay attention to the characteristics of online platforms, and how users' perceptions of the purpose of the online platform may affect their online posting behavior.

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Content

Vgl.: http%3A%2F%2Fcreativechoice.org%2Fdoc%2FHansJonas.pdf&usg=AOvVaw1TM3teaYKgABL5H9yoIifA&opi=89978449.

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Abstract

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. Opinion Mining and Sentiment Analysis covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. The focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. The survey includes an enumeration of the various applications, a look at general challenges and discusses categorization, extraction and summarization. Finally, it moves beyond just the technical issues, devoting significant attention to the broader implications that the development of opinion-oriented information-access services have: questions of privacy, vulnerability to manipulation, and whether or not reviews can have measurable economic impact. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided. Opinion Mining and Sentiment Analysis is the first such comprehensive survey of this vibrant and important research area and will be of interest to anyone with an interest in opinion-oriented information-seeking systems.

LCSH

Public opinion

Subject

Public opinion

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Abstract

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.