Sentiment analysis based on clustering: a framework in improving accuracy and recognizing neutral opinions

Li, Gang; Liu, Fei

doi:10.1007/s10489-013-0463-3

Cited by 48 publications

(30 citation statements)

References 21 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: 63%

“…Then, we compare our proposed model's results with the surveys in (Ruggieri 2002;Kretschmann et al 2001;Quinlan 1996a, b;Xiaoliang et al 2009Xiaoliang et al , 2004Korting 2006;Pan et al 2003;Sornlertlamvanich et al 2000;Rajeswari and Kannan 2008;Steven 1994;Mazid et al 2016;Muniyandi et al 2012;Mita 2011;Taboada et al 2008;Nizamani et al 2012;Wan et al 2015;Winkler et al 2015;Psomakelis et al 2015;Shrivastava and Nair 2015;Vinodhini and Chandrasekaran 2013;Voll et al 2007;Mandal et al 2014;Kaur et al 2015Kaur et al , 2016Prasad et al 2016, 27;Mugdha;Sharma 2014;Park et al 2003;Loh and Mauricio 2003, 31, 32, 33, 34;Phu and Tuoi 2014;Tran et al 2014;Li and Liu 2014;Turney 2002;Lee et al 2002;Zyl 2002;Le Hegarat-Mascle et al 2002;Ferro-Famil and Pottier 2002;Chaovalit and Zhou 2005;Lee and Lewicki 2002;Gllavata et al 2004;Phu et al 2016Phu et al , 2017a…”

Section: Related Workmentioning

confidence: 94%

“…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: 63%

Section: Related Workmentioning

confidence: 94%

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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show abstract

“…So their social interactions can be used to find the overall sentiment orientation of the blogger. Li et al [26] used opinions expressed on the web such as blogs, reviews and comments to design a new technique to further enhance the accuracy of clustering based approaches. This approach is proven to more suitable in finding neutral opinions.…”

Section: Related Workmentioning

confidence: 99%

Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

et al. 2020

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Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

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“…Li and Liu et al [19] in this paper author have made further improvement in his existing work by processing and modification in the various mechanism of sentiments for extracting the information from the huge dataset. The evaluation of results is done on three classes of balanced and unbalanced.…”

Section: Dipti Sharma Munish Sabharwalmentioning

confidence: 99%

Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

2019

IJITEE

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Sentiment analysis is an area of natural language processing (NLP) and machine learning where the text is to be categorized into predefined classes i.e. positive and negative. As the field of internet and social media, both are increasing day by day, the product of these two nowadays is having many more feedbacks from the customer than before. Text generated through social media, blogs, post, review on any product, etc. has become the bested suited cases for consumer sentiment, providing a best-suited idea for that particular product. Features are an important source for the classification task as more the features are optimized, the more accurate are results. Therefore, this research paper proposes a hybrid feature selection which is a combination of Particle swarm optimization (PSO) and cuckoo search. Due to the subjective nature of social media reviews, hybrid feature selection technique outperforms the traditional technique. The performance factors like f-measure, recall, precision, and accuracy tested on twitter dataset using Support Vector Machine (SVM) classifier and compared with convolution neural network. Experimental results of this paper on the basis of different parameters show that the proposed work outperforms the existing work

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Sentiment analysis based on clustering: a framework in improving accuracy and recognizing neutral opinions

Cited by 48 publications

References 21 publications

A C4.5 algorithm for english emotional classification

A C4.5 algorithm for english emotional classification

Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

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