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 now 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.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our 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. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

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Opinion Mining and Sentiment Analysis

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.

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Opinion Extraction, Summarization and Tracking in News and Blog Corpora.

The sentiment detection of texts has been witnessed a booming interest in recent years, due to the increased availability of online reviews in digital form and the ensuing need to organize them Till to now, there are mainly four different problems predominating in this research community, namely, subjectivity classification, word sentiment classification, document sentiment classification and opinion extraction In fact, there are inherent relations between them Subjectivity classification can prevent the sentiment classifier from considering irrelevant or even potentially misleading text Document sentiment classification and opinion extraction have often involved word sentiment classification techniques This survey discusses related issues and main approaches to these problems

A survey on sentiment detection of reviews

In the past years we have witnessed Sentiment Analysis and Opinion Mining becoming increasingly popular topics in Information Retrieval and Web data analysis. With the rapid growth of the user-generated content represented in blogs, wikis and Web forums, such an analysis became a useful tool for mining the Web, since it allowed us to capture sentiments and opinions at a large scale. Opinion retrieval has established itself as an important part of search engines. Ratings, opinion trends and representative opinions enrich the search experience of users when combined with traditional document retrieval, by revealing more insights about a subject. Opinion aggregation over product reviews can be very useful for product marketing and positioning, exposing the customers' attitude towards a product and its features along different dimensions, such as time, geographical location, and experience. Tracking how opinions or discussions evolve over time can help us identify interesting trends and patterns and better understand the ways that information is propagated in the Internet. In this study, we review the development of Sentiment Analysis and Opinion Mining during the last years, and also discuss the evolution of a relatively new research direction, namely, Contradiction Analysis. We give an overview of the proposed methods and recent advances in these areas, and we try to layout the future research directions in the field.

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Survey on mining subjective data on the web

In this introduction, we present an overview of the current state of research in the Natural Language Processing tasks of subjectivity and sentiment analysis, as well as their application domains and closely-related research field of emotion detection. Although many definitions exist for these tasks and the research done within their frame spans over approaches with different objectives, we consider subjectivity analysis to deal with the detection of ''private states'' (opinions, emotions, sentiments, beliefs, speculations) and sentiment analysis as the task of detecting, extracting and classifying opinions and sentiments concerning different topics, as expressed in textual input. After describing the key concepts and research directions in these tasks, we present the main achievements obtained so far and the issues that remain to be tackled. Subsequently, we introduce each of the papers in this volume and present their contribution to the research areas of subjectivity and sentiment analysis. Finally, we conclude on the present state of work in these fields and reflect on the possible future developments.

Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments

Major topic detection and its application to opinion summarization

Opinion retrieval aims to tell if a document is positive, neutral or negative on a given topic. Opinion extraction further identifies the document’s supportive and the non-supportive evidence. This paper defines the annotation of opinionated material. The algorithm employs opinion holders, a topic’s conceptual words, sentiment words, opinion operators, and negation operators to recognize opinions. An opinion extraction system is developed and then reflects the major views of selected information sources. The text-based evidence extracted is ready for opinion summarization and opinionated question answering.

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Construction of an Evaluation Corpus for Opinion Extraction

Construction of an Evaluation Corpus for Opinion Extraction.

The invention concerns an opinion analysis system. It employs sentiment words to determine the opinion polarity, and detects the major topics of the related events. When users input the opinion analysis requests, the system extracts the positive and the negative opinions, and provides the summarization of the events. For the data containing temporal information, the invention presents the opinion analysis results along the time axis.

Li-Ying Lee

Papers

Major topic detection and its application to opinion summarization

Construction of an Evaluation Corpus for Opinion Extraction

Construction of an Evaluation Corpus for Opinion Extraction.

Chinese opinion retrieval and extraction systems