The Importance of Neutral Examples for Learning Sentiment

Koppel, Moshe; Schler, Jonathan

doi:10.1111/j.1467-8640.2006.00276.x

Cited by 127 publications

(85 citation statements)

References 11 publications

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“…This model demonstrates the essentiality of a neutral class in sentiment analysis in the case of the Arabic language. A neutral class with generalisation of the proposed classification methods will lead to a higher classification accuracy (Koppel and Schler, 2006;Hamed et al, 2016).…”

Section: The Uniqueness Of the Arabic Languagementioning

confidence: 99%

Sentiment Analysis of Arabic Tweets in e-Learning

AL-Rubaiee

Qiu

Alomar

et al. 2016

Journal of Computer Science

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Abstract:In this study, we present the design and implementation of Arabic text classification in regard to university students' opinions through different algorithms such as Support Vector Machine (SVM) and Naive Bayes (NB). The aim of the study is to develop a framework to analyse Twitter "tweets" as having negative, positive or neutral sentiments in education or, in other words, to illustrate the relationship between the sentiments conveyed in Arabic tweets and the students' learning experiences at universities. Two experiments were carried out, one using negative and positive classes only and the other one with a neutral class. The results show that in Arabic, a sentiments SVM with an n-gram feature achieved higher accuracy than NB both with using negative and positive classes only and with the neutral class.

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Section: The Uniqueness Of the Arabic Languagementioning

confidence: 99%

Sentiment Analysis of Arabic Tweets in e-Learning

AL-Rubaiee

Qiu

Alomar

et al. 2016

Journal of Computer Science

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show abstract

“…As a matter of fact, even extra half star ratings can have dramatic economic impact (Anderson and Magruder, 2012). To predict multi-level ratings, either multiclass classification or regression methods can be applied (Koppel and Schler, 2006;Yu et al, 2013). Pang and Lee (2005) have also proposed an alternative meta-algorithm based on metric labeling for predicting three or four sentiment classes for movie reviews.…”

Section: Related Workmentioning

confidence: 99%

"My Curiosity was Satisfied, but not in a Good Way": Predicting User Ratings for Online Recipes

Liu¹,

Guo²,

Dakota³

et al. 2014

Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)

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In this paper, we develop an approach to automatically predict user ratings for recipes at Epicurious.com, based on the recipes' reviews. We investigate two distributional methods for feature selection, Information Gain and Bi-Normal Separation; we also compare distributionally selected features to linguistically motivated features and two types of frameworks: a one-layer system where we aggregate all reviews and predict the rating vs. a two-layer system where ratings of individual reviews are predicted and then aggregated. We obtain our best results by using the two-layer architecture, in combination with 5 000 features selected by Information Gain. This setup reaches an overall accuracy of 65.60%, given an upper bound of 82.57%.

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“…The overall theme sentiment polarity is neutral. According to Koppel and Schler, neutral improves the overall accuracy and should not be considered as a state between positive and negative but as a separate class that denotes the lack of sentiment [38]. The sentence the weather is hot" for example, cannot be considered negative or positive.…”

Section: Theme Extraction From Digital Transcriptsmentioning

confidence: 99%

“…The results of the study can greatly help improve the teaching environment. The International Journal of E-Learning and Educational Technologies in the Digital Media (IJEETDM) 1(1): [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49] The Society of Digital Information and Wireless Communication (SDIWC), 2014 (ISSN: 2410-0439)…”

Section: Summative Examinationmentioning

confidence: 99%

“…The International Journal of E-Learning and Educational Technologies in the Digital Media (IJEETDM) 1(1): [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49] The Society of Digital Information and Wireless Communication (SDIWC), Jan -2015 (ISSN: about the learner's progress and support them in selecting appropriate learning tasks. The consistency of the content, methodology, and the manner of assessment will make teaching become more effective.…”

Section: Introductionmentioning

confidence: 99%

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Improving Learning Throughput in E-Learning Using Interactive-Cognitive Based Assessment

Ballera¹,

Lukandu

Radwan³

2015

IJEETDM

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An e-learning website is not sufficient to fully attain the results of online education. There also is a need to align the educational objectives into the design of the assessment to improve and develop cognition, critical thinking and problem-solving skills. Previous studies have explored the potentials of the assessment models but few ventured into their implementation. Others only proposed and introduced conceptual frameworks. The implementation of these proposals, however, revealed that the question type in the assessment phase neglected to align their questionnaire formats into a cognitive schema. At present, the standard multiple-choice question is the most frequently used of the question type of e-learning assessments. However, if this type is the only format adopted by e-learning developers, then the potentially rich and embedded assessment of the computer platform will be given up. This paper focuses on the design of assessment questions, which is created and guided by the hierarchical Bloom cognitive taxonomy and by utilizing rich media formats. Results conducted for eighteen weeks show a dramatic increase in the academic performance of the students. Likewise, digital transcripts converted from the collected perceptions after training undergoes sentiment analysis have correlated with the student improved academic throughput.

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The Importance of Neutral Examples for Learning Sentiment

Cited by 127 publications

References 11 publications

Sentiment Analysis of Arabic Tweets in e-Learning

Sentiment Analysis of Arabic Tweets in e-Learning

"My Curiosity was Satisfied, but not in a Good Way": Predicting User Ratings for Online Recipes

Improving Learning Throughput in E-Learning Using Interactive-Cognitive Based Assessment

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