Ensemble of feature sets and classification algorithms for sentiment classification

Xia, Rui; Zong, Chengqing; Li, Shoushan

doi:10.1016/j.ins.2010.11.023

Cited by 521 publications

(237 citation statements)

References 31 publications

(33 reference statements)

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“…Random Forests Model is an ensemble learning method of the classification of dependent variables by constructing multiple decision trees during the training period [75]. To get a more accurate and stable prediction p(c|f) through maximum voting, the algorithm amalgamate several decision trees from inputting the variables Feature (f) that build multiple decision trees Tree t n , · · · , Tree t n .…”

Section: Appendix B3 Random Forests Modelmentioning

confidence: 99%

Understanding Household Waste Separation Behaviour: Testing the Roles of Moral, Past Experience, and Perceived Policy Effectiveness within the Theory of Planned Behaviour

et al. 2017

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Due to the increasing waste generation over the years in China, there is an urgent need to addressing this major problem by implementing effective household waste separation programs. Although past studies have tried to explain the waste behaviour from social and psychological motivations, there is little understanding as to the impact of individual moral obligation and past experience on forming waste separating intention. The aim of this study is to investigate key determinants influencing household waste separation intention and behaviour. Based on the theory of planned behaviour (TPB) model, we designed a survey questionnaire by taking account of determinants including residents' attitude, subjective norm, perceived behavioural control, moral obligation, past behaviour, and demographic factors, with the prediction of household waste separation behaviour in mind. After obtaining 628 valid questionnaires from households in the city of Hangzhou, the partial least squares structural equation modelling (PLS-SEM) was conducted to evaluate the effectiveness of each construct. The results indicate that subjective norms, perceived behavioural control, past behaviour and intention significantly predict household waste separation behaviour, with past behaviour being the most significant construct to predict individuals' intention and behaviour. Additional analysis on the moderating effect of different kinds of people in terms of their genders, ages, income levels and perceived policy effectiveness, to further evoke household waste separation behaviours is also discussed. The findings suggest insightful future policies that can focus on residents' habit formation by providing the convenient location of waste separation and collection facilities, encouraging market-driven recycling programs and traders, promoting community campaigns and education which help residents to form favourable habits for protecting the environment.

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Section: Appendix B3 Random Forests Modelmentioning

confidence: 99%

Understanding Household Waste Separation Behaviour: Testing the Roles of Moral, Past Experience, and Perceived Policy Effectiveness within the Theory of Planned Behaviour

et al. 2017

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“…Corpus-based approach: It can be implemented in two ways. In the first type, a basic seed list of general-purpose sentiment words is present and other sentiment words and their orientations from a domain corpus are obtained [19].…”

Section: Lexicon Based Approachmentioning

confidence: 99%

A Lexical Approach for Opinion Mining in Twitter

Mumtaz¹,

Ahuja

2016

IJEME

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The blossoming of a significant number of social networking sites, blogs, and microblogs has given a podium for general masses to voice their opinion regarding social topics, economic issues, political matters, market trends etc. However, this sudden eruption of review data had opened floodgates to unmanageable records as it is almost impossible for any individual or organization to manually extract any useful information from it. Opinion mining or sentimental analysis is a natural language processing which can obtain the opinion or feeling of people about any particular product or subject. The main focus of this paper is to find a method to perform sentiment analysis of Twitter which is one of the most prevalent microblogging sites. The lexical method proposed in this paper classifies the tweets as positive, negative or neutral depending on the polarity of the words in it. Also, the role of negation words has been investigated.

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“…Naïve Bayes algorithm is the most widely used one and it is a simple but effective supervised classification method [7]. On the other hand, Support Vector Machine (SVM) is also tested in this study as it is a more efficient algorithm in sentiment classification [8]. Along with this two algorithm, one decision tree method, namely, J48 (using WEKA data mining software tool) was also investigated.…”

Section: A Classification Algorithmsmentioning

confidence: 99%

Sentiment lexicon construction using SentiWordNet 3.0

Medagoda

Shanmuganathan

Whalley

2015

2015 11th International Conference on Natural Computation (ICNC)

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Abstract-Opinion mining and sentiment analysis have become popular in linguistic resource rich languages. Opinions for such analysis are drawn from many forms of freely available online/ electronic sources, such as websites, blogs, news re-ports and product reviews. But attention received by less resourced languages is significantly less. This is because the success of any opinion mining algorithm depends on the availability of resources, such as special lexicon and WordNet type tools. In this research, we implemented a less complicated but an effective approach that could be used to classify comments in less resourced languages. We experimented the approach for use with Sinhala Language where no such opinion mining or sentiment analysis has been carried out until this day. Our algorithm gives significantly promising results for analyzing sentiments in Sinhala for the first time.

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Ensemble of feature sets and classification algorithms for sentiment classification

Cited by 521 publications

References 31 publications

Understanding Household Waste Separation Behaviour: Testing the Roles of Moral, Past Experience, and Perceived Policy Effectiveness within the Theory of Planned Behaviour

Understanding Household Waste Separation Behaviour: Testing the Roles of Moral, Past Experience, and Perceived Policy Effectiveness within the Theory of Planned Behaviour

A Lexical Approach for Opinion Mining in Twitter

Sentiment lexicon construction using SentiWordNet 3.0

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