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Tagging Heterogeneous Evaluation Corpora for Opinionated Tasks
"... Opinion retrieval aims to tell if a document is positive, neutral or negative on a given topic. Opinion extraction further identifies the supportive and the non-supportive evidence of a document. To evaluate the performance of proposed technologies, a suitable corpus is necessary for opinionated tas ..."
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Opinion retrieval aims to tell if a document is positive, neutral or negative on a given topic. Opinion extraction further identifies the supportive and the non-supportive evidence of a document. To evaluate the performance of proposed technologies, a suitable corpus is necessary for opinionated tasks. This paper defines the annotations for opinionated materials. Heterogeneous experimental materials are annotated, and the agreements among annotators are analyzed. How human can monitor opinions of the whole is also examined. The corpus can be employed to opinion extraction, opinion summarization, opinion tracking and opinionated question answering. 1.
Scope of negation detection in sentiment analysis
, 2011
"... An important part of information-gathering behaviour has always been to find out what other people think and whether they have favourable (positive) or unfavourable (negative) opinions about the subject. This survey studies the role of negation in an opinion-oriented information-seeking system. We i ..."
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An important part of information-gathering behaviour has always been to find out what other people think and whether they have favourable (positive) or unfavourable (negative) opinions about the subject. This survey studies the role of negation in an opinion-oriented information-seeking system. We investigate the problem of determining the polarity of sentiments in movie reviews when negation words, such as not and hardly occur in the sentences. We examine how different negation scopes (window sizes) affect the classification accuracy. We used term frequencies to evaluate the discrimination capacity of our system with different window sizes. The results show that there is no significant difference in classification accuracy when different window sizes have been applied. However, negation detection helped to identify more opinion or sentiment carrying expressions. We conclude that traditional negation detection methods are inadequate for the task of sentiment analysis in this domain and that progress is to be made by exploiting information about how opinions are expressed implicitly.
Novel Relationship Discovery Using Opinions Mined from the Web
"... This paper proposes relationship discovery models using opinions mined from the Web instead of only conventional collocations. Web opinion mining extracts subjective information from the Web for specific targets, summarizes the polarity and the degree of the information, and tracks the development o ..."
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This paper proposes relationship discovery models using opinions mined from the Web instead of only conventional collocations. Web opinion mining extracts subjective information from the Web for specific targets, summarizes the polarity and the degree of the information, and tracks the development over time. Targets which gain similar opinionated tendencies within a period of time may be correlated. This paper detects event bursts from the tracking plots of opinions, and decides the strength of the relationship using the coverage of the plots. Companies are selected as the experimental targets. A total of 1,282,050 economics-related documents are collected from 93 Web sources between August 2003 and May 2005 for experiments. Models that discover relations are then proposed and compared on the basis of their performance. There are three types of models, collocation-based, opinionbased, and integration models, and respectively, four, two and two variants of each type. For evaluation, company pairs which demonstrate similar oscillation of stock prices are considered correlated and are selected as the gold standard. The results show that collocation-based models and opinion-based models are complementary, and the integration models perform the best. The top 25, 50 and 100 answers discovered by the best integration model achieve precision rates of 1, 0.92 and 0.79, respectively.
The 5W structure for sentiment summarization-visualization-tracking
- In Proceeding of the 13th International Conference on Intelligent Text Processing and Computational Linguistics (CICLING
, 2012
"... Abstract. In this paper we address the Sentiment Analysis problem from the end user’s perspective. An end user might desire an automated at-a-glance presentation of the main points made in a single review or how opinion changes time to time over multiple documents. To meet the requirement we propose ..."
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Abstract. In this paper we address the Sentiment Analysis problem from the end user’s perspective. An end user might desire an automated at-a-glance presentation of the main points made in a single review or how opinion changes time to time over multiple documents. To meet the requirement we propose a relatively generic opinion 5Ws structurization, further used for textual and visual summary and tracking. The 5W task seeks to extract the semantic constituents in a natural language sentence by distilling it into the answers to the 5W questions: Who, What, When, Where and Why. The visualization system facilitates users to generate sentiment tracking with textual summary and sentiment polarity wise graph based on any dimension or combination of dimensions as they want i.e. “Who ” are the actors and “What ” are their sentiment regarding any topic, changes in sentiment during “When ” and “Where ” and the reasons for change in sentiment as “Why”.
Opinion Extraction Applied to Criteria
"... Abstract. The success of Information technologies and associated services (e.g., blogs, forums,...) eases the way to express massive opinion on various topics. Recently new techniques known as opinion mining have emerged. One of their main goals is to automatically extract a global trend from expres ..."
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Abstract. The success of Information technologies and associated services (e.g., blogs, forums,...) eases the way to express massive opinion on various topics. Recently new techniques known as opinion mining have emerged. One of their main goals is to automatically extract a global trend from expressed opinions. While it is easy to get this overall assessment, a more detailed analysis will highlight that the opinions are expressed on more specific topics: one will acclaim a movie for its soundtrack and another will criticize it for its scenario. Opinion mining approaches have little explored this multicriteria aspect. In this paper we propose an automatic extraction of text segments related to a set of criteria. The opinion expressed in each text segment is then automatically extracted. From a small set of opinion keywords, our approach automatically builds a training set of texts from the web. A lexicon reflecting the polarity of words is then extracted from this training corpus. This lexicon is then used to compute the polarity of extracted text segments. Experiments show the efficiency of our approach. 1
Tourism-Related Opinion Mining
"... This paper focuses on the tourism-related opinion mining, including tourism-related opinion detection and tourist attraction target identification. The experimental data are blog articles labeled as in the domestic tourism category in a blogspace. Annotators were asked to annotate the opinion polari ..."
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This paper focuses on the tourism-related opinion mining, including tourism-related opinion detection and tourist attraction target identification. The experimental data are blog articles labeled as in the domestic tourism category in a blogspace. Annotators were asked to annotate the opinion polarity and the opinion target for every sentence. Different strategies and features have been proposed to identify opinion targets, including tourist attraction keywords, coreferential expressions, tourism-related opinion words, a 2-level classifier, and so on. We used machine learning methods to train classifiers for tourism-related opinion mining. A retraining mechanism was proposed to obtain the system decisions of preceding sentences as a new feature. The precision and recall scores of tourism-related opinion detection were 55.98 % and 59.30%, respectively, and the scores of tourist attraction target identification among known tourism-related opinionated sentences were 90.06 % and 89.91%, respectively. The overall precision and recall scores were 51.30 % and 54.21%, respectively.
Tourism-Related Opinion Detection and Tourist-Attraction Target Identification
"... This paper focuses on tourism-related opinion mining, including tourism-related opinion detection and tourist-attraction target identification. The experimental data are blog articles labeled as being in the domestic tourism category in a blogspace. Annotators were asked to annotate the opinion pola ..."
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This paper focuses on tourism-related opinion mining, including tourism-related opinion detection and tourist-attraction target identification. The experimental data are blog articles labeled as being in the domestic tourism category in a blogspace. Annotators were asked to annotate the opinion polarity and the opinion target for every sentence. Different strategies and features have been proposed to identify
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"... This paper proposes relationship discovery models using opinions mined from the Web instead of only conventional collocations. Web opinion mining extracts subjective information from the Web for specific targets, summarizes the polarity and the degree of the information, and tracks the development o ..."
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This paper proposes relationship discovery models using opinions mined from the Web instead of only conventional collocations. Web opinion mining extracts subjective information from the Web for specific targets, summarizes the polarity and the degree of the information, and tracks the development over time. Targets which gain similar opinionated tendencies within a period of time may be correlated. This paper detects event bursts from the tracking plots of opinions, and decides the strength of the relationship using the coverage of the plots. Companies are selected as the experimental targets. A total of 1,282,050 economics-related documents are collected from 93 Web sources between August 2003 and May 2005 for experiments. Models that discover relations are then proposed and compared on the basis of their performance. There are three types of models, collocation-based, opinionbased, and integration models, and respectively, four, two and two variants of each type. For evaluation, company pairs which demonstrate similar oscillation of stock prices are considered correlated and are selected as the gold standard. The results show that collocation-based models and opinion-based models are complementary, and the integration models perform the best. The top 25, 50 and 100 answers discovered by the best integration model achieve precision rates of 1, 0.92 and 0.79, respectively.
MetaBrain: Web Information Extraction and Visualization
"... Nowadays, the web is a huge source of information on different branches of knowledge. This knowledge, however, is dispersed across many sites, making it difficult to interrelate and understand. In the past few years some approaches have been developed to ease the extraction of this information, from ..."
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Nowadays, the web is a huge source of information on different branches of knowledge. This knowledge, however, is dispersed across many sites, making it difficult to interrelate and understand. In the past few years some approaches have been developed to ease the extraction of this information, from Open Information Extraction to simpler data mining. Usually these solutions work as standalone applications and are developed from scratch and are brittle, very sensitive to changes in the data sources. This makes it difficult for the final user to fully explore the potential of using different algorithms together to better extract and analyze information. In this paper we propose a new approach where users can create their own personalized information extractors and visualizations, without needing to type a single line of code, in an easy and highly flexible manner using a special-purpose interface. Since raw data is most times difficult to understand, we also study how the user can create customized visualizations of this extracted data with low effort. A prototype of this concept, MetaBrain, has been implemented and tested. Preliminary heuristics evaluation, demonstrate favorable results for the concept.
