A faceted characterization of the opinion mining landscape

Arora, Rajeev; Srinivasa, Srinath

doi:10.1109/comsnets.2014.6734936

Cited by 6 publications

(6 citation statements)

References 38 publications

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“…Guo et al [30] define the concept of "Public Opinion Mining," compare different approaches used in each step of the OM pipeline and propose future directions for the field. In [20] the authors propose a faceted characterization of Opinion Mining composed of two main branches, namely opinion structure which deals with the relation between unstructured subjective text and structured conceptual elements, and Opinion Mining tools and techniques which are the means to achieve the OM task. They also tackle the problems of entity discovery and aspect identification, lexicon acquisition and sarcasm detection.…”

Section: Opinion Miningmentioning

confidence: 99%

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Opinion Mining and Information Fusion: A survey

2016

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Section: Opinion Miningmentioning

confidence: 99%

“…The usual Opinion Mining process or pipeline usually consists of a series of defined steps [20,21,22]. These correspond to corpus or data acquisition, text preprocessing, Opinion Mining core process, aggregation and summarization of results, and visualization.…”

Section: Opinion Mining Process: Previous Stepsmentioning

confidence: 99%

Opinion Mining and Information Fusion: A survey

2016

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“…Sentiment analysis has been conducted at different granularities, from the document and sentence levels to the word level. Several books and paper reviews on this subject have been published (Arora & Srinivasa, ; Liu, ).…”

Section: Related Workmentioning

confidence: 99%

Implicit opinion analysis: Extraction and polarity labelling

Huang

Wang

Chen

2017

Asso for Info Science & Tech

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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 approachesdeep 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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“…That's mean opinions may be classified into positive, negative, or neutral. Moreover, there is a forth type which is a constructive opinion which obtains suggestion to make the product better [7]. Opinions are classified into three categories: the first one is direct opinions which opinion holder directly attack to target.…”

Section: Sentiment Classificationmentioning

confidence: 99%

Sentiment Analysis and Opinion Mining: A Survey

Alhojely¹

2016

IJCA

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Opining mining has gained high level focus in business and commercial fields. The opinions are considered as valuable data. Opinions are customer reviews, comments and feelings and collected from web forums, websites, and user groups which are posted by thousands of end users and also recorded manually. The collected opinions are processed by various techniques, algorithms, methods and software tools in order to extract the meaning from them. Extraction of meaningful information from opinions is given more importance in the field of business analytics. This entire process is popularly known as opinion mining or sentiment analysis and they are used in industries to develop quality of services, products. This article is an outcome of a study which made on various sub tasks, approaches, methods and techniques involved and applied in opinion mining.

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A faceted characterization of the opinion mining landscape

Cited by 6 publications

References 38 publications

Opinion Mining and Information Fusion: A survey

Opinion Mining and Information Fusion: A survey

Implicit opinion analysis: Extraction and polarity labelling

Sentiment Analysis and Opinion Mining: A Survey

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