Mining opinion components from unstructured reviews: A review

Khan, Khairullah; Baharudin, Baharum; Khan, Aurnagzeb; Ullah, Ashraf

doi:10.1016/j.jksuci.2014.03.009

Cited by 94 publications

(41 citation statements)

References 83 publications

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“…The OM has been categorized in subproblems as explained by [4]. Opinion words and opinion targets have been identified in different ways.…”

Section: Literature Reviewmentioning

confidence: 99%

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Impact of Anaphora Resolution on Opinion Target Identification

Saqia¹,

Khan²,

Khan³

et al. 2018

ijacsa

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Abstract-Opinion mining is an interesting area of research because of its wide applications in the decision-making process. Opinion mining aims to extract user's perception from the text and to create a fast and accurate summary of people's opinion about anything. In this study, we have worked on opinion target identification and the impact of anaphora resolution on opinion target extraction. Anaphora resolution can be utilized to detect opinion target in sentences having prepositions instead of nouns. We empirically evaluated the impact of anaphora resolution using benchmark datasets. We have achieved accuracy such as precision: 88.14 recall: 71.45 and f-score: 72.12, respectively.

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“…The OM has been categorized in subproblems as explained by [4]. Opinion words and opinion targets have been identified in different ways.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Opinion has different components. Identification of each component from free text is a challenging task [3], [4]. This study is about opinion target identification.…”

Section: Introductionmentioning

confidence: 99%

Impact of Anaphora Resolution on Opinion Target Identification

Saqia¹,

Khan²,

Khan³

et al. 2018

ijacsa

Self Cite

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“…K. Khan et al [7] reviews the various advancement in opinion mining research. According to Tsytsarau et al [8] opinion mining methods can be classified in four approaches: Machine Learning, Dictionary, Statistical, and Semantic.…”

Section: Literature Surveymentioning

confidence: 99%

Feature based opinion mining: A survey

Ganeshbhai¹,

Shah²

2015

2015 IEEE International Advance Computing Conference (IACC)

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In olden days people were only information consumers but since advent of Web 2.0 they plays more important role in publishing information on Web in the form of comments and reviews. The user generated content forced organization to pay attention towards analyzing this content for better visualization of public's opinion. Opinion mining or Sentiment analysis is an autonomous text analysis and summarization system for reviews available on Web. Opinion mining aims for distinguishing the emotions expressed within the reviews, classifying them into positive or negative and summarizing into the form that is quickly understood by users. Feature based opinion mining performs fine-grain analysis by recognizing individual features of an object upon which user has expressed opinion. This paper gives an insight of various methods proposed in the area of feature based opinion mining and also discuss the limitations of existing work and future direction in feature based opinion mining.

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“…It utilizes social network and web blogs which are the most common sources for opinion recovery. And numerous computational techniques and linguistic characteristics associated with opinion mining, component analyses and opinion target analysis are examined and explained [7]. This paper focuses on studying the ever growing amount of available construction data, developing data mining, knowledge innovation and establishing the massive available construction information with the advancement of better data recovery techniques.…”

Section: Related Workmentioning

confidence: 99%

An efficient unstructured big data analysis method for enhancing performance using machine learning algorithm

Reshmy

Paulraj²

2015

2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015]

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In this modern world, data mining technology holds an essential position in all the major Engineering fields. Handling of Unstructured Big Data is an essential task of this era. At present, making the maximum advantage of parallel processing know-hows and the task of rapid examination of huge data steadily and continuously transmitted or received from various sources is becoming popular or conventional. The big data analytics job is fragmented into smaller jobs and ran over tens, hundreds or thousands of product servers by the parallel processing architecture. This helps in maintaining the data center cost efficient and facilitates easy handling of the enormous work in an efficient way. In this paper, proposed solution takes online consumer purchase. The online system has unrivalled bank of data on online consumer purchasing behavior that can be mined from its 100 million customers accounts. They use customer click-stream data and historical purchase data of all those 100 million customers and each user is shown personalized results on customized web pages. For improving Big Data performance the Machine Learning Method i.e. K-Nearest Neighbour algorithm used to support to take good analysis. Hadoop simulator is used to solve this kind of task.

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Mining opinion components from unstructured reviews: A review

Cited by 94 publications

References 83 publications

Impact of Anaphora Resolution on Opinion Target Identification

Impact of Anaphora Resolution on Opinion Target Identification

Feature based opinion mining: A survey

An efficient unstructured big data analysis method for enhancing performance using machine learning algorithm

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