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article

Survey on mining subjective data on the web

Authors:

Mikalai Tsytsarau,

Themis PalpanasAuthors Info & Claims

Data Mining and Knowledge Discovery, Volume 24, Issue 3

Pages 478 - 514

https://doi.org/10.1007/s10618-011-0238-6

Published: 01 May 2012 Publication History

Abstract

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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cover image Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery Volume 24, Issue 3

May 2012

265 pages

ISSN:1384-5810

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Copyright © Copyright © 2012 The Author(s).

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Kluwer Academic Publishers

United States

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Published: 01 May 2012

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https://dl.acm.org/doi/10.1007/s10115-024-02200-9
Ma HYarosh S(2023)A Review of Affective Computing Research Based on Function-Component-Representation FrameworkIEEE Transactions on Affective Computing10.1109/TAFFC.2021.310451214:2(1655-1674)Online publication date: 1-Apr-2023
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https://dl.acm.org/doi/10.1007/s42979-021-00480-4
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