Mine the easy, classify the hard

Dasgupta, Sajib; Ng, Vincent

doi:10.3115/1690219.1690244

Cited by 115 publications

(2 citation statements)

References 14 publications

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“…For instance, Pang and Lee (2004) examined the relation between subjectivity and polarity classification; Kennedy and Inkpen (2006) identified three types of valence shifters: negations, intensifiers, and diminishers; and Wilson et al (2009) explored the difference between prior and contextual polarity and recognized the importance of identifying neutral instances. Some studies deal with scenarios for sentiment classification that are more complicated, such as cross-domain adaptation (Blitzer et al, 2007; and semi-supervised learning (Dasgupta & Ng 2009;Li et al, 2010).…”

Section: A Emotion Analysismentioning

confidence: 99%

Emotion Analysis in Code-Switching Text With Joint Factor Graph Model

Wang

Lee

et al. 2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Previous research on emotions analysis have placed much emphasis in monolingual instead of bilingual text. However, emotions on social media platforms are often found in bilingual or code-switching posts. Different from monolingual text, emotions in code-switching text can be expressed in both monolingual or bilingual forms. Moreover, more than one emotion can be expressed within a single post; yet they tend to be related in some ways which offers some implications. It is thus necessary to consider the correlation between different emotions. In this paper, a joint factor graph model is proposed to address this issue. In particular, attribute functions of the factor graph model are utilized to learn both monolingual and bilingual information from each post, factor functions are used to explore the relationship among different emotions, and a belief propagation algorithm is employed to learn and predict the model. Empirical studies demonstrate the importance of emotion analysis in code-switching text and the effectiveness of our proposed joint learning model.

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Section: A Emotion Analysismentioning

confidence: 99%

Emotion Analysis in Code-Switching Text With Joint Factor Graph Model

Wang

Lee

et al. 2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…Pang et al [3] used machine learning methods such as naive Bayesian classifier, maximum entropy classifier and support vector machine (SVM) to train unigram, bigram, position features and part-of-speech (POS) tag to distinguish comments as positive or negative sentiment. Dasgupta and Ng [4] considered the occurrence of emotionally ambiguous comments and proposed a semi-supervised method. Onan et al [5] combined keyword-based text document representation with integrated learning to improve the predictive performance of scientific text document classification.…”

Section: Introductionmentioning

confidence: 99%

A novel scheme of domain transfer in document-level cross-domain sentiment classification

Lei

2021

Journal of Information Science

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The sentiment classification aims to learn sentiment features from the annotated corpus and automatically predict the sentiment polarity of new sentiment text. However, people have different ways of expressing feelings in different domains. Thus, there are important differences in the characteristics of sentimental distribution across different domains. At the same time, in certain specific domains, due to the high cost of corpus collection, there is no annotated corpus available for the classification of sentiment. Therefore, it is necessary to leverage or reuse existing annotated corpus for training. In this article, we proposed a new algorithm for extracting central sentiment sentences in product reviews, and improved the pre-trained language model Bidirectional Encoder Representations from Transformers (BERT) to achieve the domain transfer for cross-domain sentiment classification. We used various pre-training language models to prove the effectiveness of the newly proposed joint algorithm for text-ranking and emotional words extraction, and utilised Amazon product reviews data set to demonstrate the effectiveness of our proposed domain-transfer framework. The experimental results of 12 different cross-domain pairs showed that the new cross-domain classification method was significantly better than several popular cross-domain sentiment classification methods.

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