Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

Manek, Asha S; Shenoy, P. Deepa; Mohan, Mahesh; R, Venugopal K.

doi:10.1007/s11280-015-0381-x

Cited by 269 publications

(133 citation statements)

References 17 publications

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“…In many works related to the sentiment classification in the world and in (Manek et al 2016;Agarwal and Mittal 2016a, b;Canuto et al 2016, Kaur et al 2016Phu 2014;Tran et al 2014;Li and Liu 2014), there is not any CArelated study for sentiment classification, which is similar to our model.…”

Section: Introductionmentioning

confidence: 64%

“…The latest researches of the sentiment classification are (Manek et al 2016;Agarwal and Mittal 2016a, b, 34;Kaur et al2016;Phu 2014;Tran et al 2014;Li and Liu 2014;Phu et al 2017a, b;Phu et al 2017). With the rapid development of the World Wide Web in (Manek et al 2016), electronic word-of-mouth interaction has made consumers active participants.…”

Section: Related Workmentioning

confidence: 99%

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A C4.5 algorithm for english emotional classification

Ngoc

et al. 2017

Evolving Systems

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negative polarity are created by the decision tree. Classifying sentiments of one English document is identified based on the association rules of the positive polarity and the negative polarity. Our English testing data set has 25,000 English documents, including 12,500 English positive reviews and 12,500 English negative reviews. We have tested our new model on our testing data set and we have achieved 60.3% accuracy of sentiment classification on this English testing data set.

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Section: Introductionmentioning

confidence: 64%

Section: Related Workmentioning

confidence: 99%

A C4.5 algorithm for english emotional classification

Ngoc

et al. 2017

Evolving Systems

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“…On [28], several multimodal sentiment analysis methods are reviewed. Finally, in [16], sentiment analysis is applied in movie reviews. …”

Section: Sentiment Analysismentioning

confidence: 99%

Analysing discussions in social networks using group decision making methods and sentiment analysis

Morente-Molinera

Kou

Peng

et al. 2018

Information Sciences

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Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in:Information Sciences 447 (2018) AbstractSocial networks are one of the most preferred environments for people to carry out debates. Due to the fact that a high amount of people can participate in the process, there is a need of tools that can analyse these discussions and extract useful information from them. In this paper, a novel way of determining how the debate is going on, if there is consensus among the participants and which alternatives are preferred is presented. Sentiment analysis is used in order to measure the level of preference that social media users have about a certain set of alternatives. In order to test the presented scheme, a real application example that makes use of Twitter information is presented.

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“…Sharma & Dey [13] provided a hybrid sentiment classification paradigm depending on boosted SVM. The intended paradigm model exploits the performance of classification of two methods (Boosting and SVM) used for sentiment based online review classification.…”

Section: Relatrd Workmentioning

confidence: 99%

Eco Inspired Bees: A Novel Feature Selection Mechanism for Sentiment Analysis

Sharmila¹,

Sivajothi²

2017

Int. J. of Pure and Appl. Math.

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Sentiment analysis also called as opinion mining is defined as the programmed learning of public's opinion, evaluations, and sentiment on different events, objects as well as their respective properties. Recently, it created high impact in academia and industry due to number of growing research problems and various applications. Selecting the features play a vital role in supreme data mining tasks since it assists in the reduction of data dimensionality by leaving the non-relevant features behind. Present work applies the ecological concept of habitat, ecological relationship and ecological succession for building an Ecology-Inspired Optimization algorithm named as Ecology Inspired Artificial Bee Colony algorithm (EI-ABC).Proposed method uses various population of candidate solution which cooperates and coevolves with one another, as per the given meta-heuristic algorithm. Sentiments are classified by using two classifiers viz., K Nearest Neighbor (KNN) and Classification and Regression Tree (CART). Result suggests that the EI-ABC algorithm proves to be interesting alternative for numerical optimization.

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Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

Cited by 269 publications

References 17 publications

A C4.5 algorithm for english emotional classification

A C4.5 algorithm for english emotional classification

Analysing discussions in social networks using group decision making methods and sentiment analysis

Eco Inspired Bees: A Novel Feature Selection Mechanism for Sentiment Analysis

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