Learn#: A Novel incremental learning method for text classification

Shan, Guangxu; Xu, Shiyao; Li, Yang; Jia, Shengbin; Xiang, Yang

doi:10.1016/j.eswa.2020.113198

Cited by 30 publications

(20 citation statements)

References 26 publications

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“…In particular, the methods proposed in [16,17] reach a macro-averaged f1-score of 0.60 and 0.69 respectively, that is definitely below that shown by our method, thus confirming that lexicon-based approaches are generally less competitive than corpus-based ones. Instead, the deep learning model proposed in [23] achieves a f1-score between 0.80 and 0.89, which is actually comparable to that of our approach (at least for the Italian). However, it should be noted that such model was not aimed at CRM communications but at product reviews, types of documents that generally show a greater level of homogeneity.…”

Section: K-fold Cross Validationsupporting

confidence: 54%

“…A more general incremental learning approach for text classification, based on deep learning with a reinforcement learning module, has only recently been proposed in [23] and applied to product reviews. The method was able to reach an f1score of 0.80 on a set of about 100,000 product reviews from Amazon and an f1-score of 0.89 on a set of the same size of Yelp reviews for the positive/negative polarity detection task.…”

Section: Related Workmentioning

confidence: 99%

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Sentiment analysis for customer relationship management: an incremental learning approach

et al. 2020

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In recent years there has been a significant rethinking of corporate management, which is increasingly based on customer orientation principles. As a matter of fact, customer relationship management processes and systems are ever more popular and crucial to facing today’s business challenges. However, the large number of available customer communication stimuli coming from different (direct and indirect) channels, require automatic language processing techniques to help filter and qualify such stimuli, determine priorities, facilitate the routing of requests and reduce the response times. In this scenario, sentiment analysis plays an important role in measuring customer satisfaction, tracking consumer opinion, interacting with consumers and building customer loyalty. The research described in this paper proposes an approach based on Hierarchical Attention Networks for detecting the sentiment polarity of customer communications. Unlike other existing approaches, after initial training, the defined model can improve over time during system operation using the feedback provided by CRM operators thanks to an integrated incremental learning mechanism. The paper also describes the developed prototype as well as the dataset used for training the model which includes over 30.000 annotated items. The results of two experiments aimed at measuring classifier performance and validating the retraining mechanism are also presented and discussed. In particular, the classifier accuracy turned out to be better than that of other algorithms for the supported languages (macro-averaged f1-score of 0.89 and 0.79 for Italian and English respectively) and the retraining mechanism was able to improve the classification accuracy on new samples without degrading the overall system performance.

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Section: K-fold Cross Validationsupporting

confidence: 54%

Section: Related Workmentioning

confidence: 99%

Sentiment analysis for customer relationship management: an incremental learning approach

et al. 2020

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“…We review some of the recent methods (Mourão et al, 2018;Kim et al, 2019;Elnagar, Al-Debsi & Einea, 2020;Shan et al, 2020;Silva et al, 2020) to represent and classify news documents in other languages such as English, Portuguese and so on. Mourão et al (2018) proposed a novel method, called Net-Class, to represent and classify the news documents in English language.…”

Section: Non-nepali News Document Representation Methodsmentioning

confidence: 99%

“…They used Recurrent Neural Networks and CNNs for features extraction and classification purposes. Shan et al (2020) proposed an incremental learning strategy based on a deep learning approach to represent and classify English news documents. Silva et al (2020) performed Portuguese news documents classification to capture fake news.…”

Section: Non-nepali News Document Representation Methodsmentioning

confidence: 99%

“…Because of the rise of social media and scattered news online, an automated document classification has been an important research domain to predict the trending news online automatically. Recently, there have been several works (Mourão et al, 2018;Kim et al, 2019;Elnagar, Al-Debsi & Einea, 2020;Shan et al, 2020; Inverse Document Frequency (IDF) to weight each token present in the document. Nevertheless, such methods still lack the strategy to eliminate the outliers, which exacerbate the classification performance in the end (Aryal et al, 2015(Aryal et al, , 2019.…”

Section: Introductionmentioning

confidence: 99%

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Vector representation based on a supervised codebook for Nepali documents classification

Sitaula

Basnet²,

Aryal

2021

PeerJ Computer Science

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Document representation with outlier tokens exacerbates the classification performance due to the uncertain orientation of such tokens. Most existing document representation methods in different languages including Nepali mostly ignore the strategies to filter them out from documents before learning their representations. In this article, we propose a novel document representation method based on a supervised codebook to represent the Nepali documents, where our codebook contains only semantic tokens without outliers. Our codebook is domain-specific as it is based on tokens in a given corpus that have higher similarities with the class labels in the corpus. Our method adopts a simple yet prominent representation method for each word, called probability-based word embedding. To show the efficacy of our method, we evaluate its performance in the document classification task using Support Vector Machine and validate against widely used document representation methods such as Bag of Words, Latent Dirichlet allocation, Long Short-Term Memory, Word2Vec, Bidirectional Encoder Representations from Transformers and so on, using four Nepali text datasets (we denote them shortly as A1, A2, A3 and A4). The experimental results show that our method produces state-of-the-art classification performance (77.46% accuracy on A1, 67.53% accuracy on A2, 80.54% accuracy on A3 and 89.58% accuracy on A4) compared to the widely used existing document representation methods. It yields the best classification accuracy on three datasets (A1, A2 and A3) and a comparable accuracy on the fourth dataset (A4). Furthermore, we introduce the largest Nepali document dataset (A4), called NepaliLinguistic dataset, to the linguistic community.

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When and How to Update Online Analytical Models for Predicting Students Performance?

Labba

Boyer

2022

Lecture Notes in Computer Science

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Learn#: A Novel incremental learning method for text classification

Cited by 30 publications

References 26 publications

Sentiment analysis for customer relationship management: an incremental learning approach

Sentiment analysis for customer relationship management: an incremental learning approach

Vector representation based on a supervised codebook for Nepali documents classification

When and How to Update Online Analytical Models for Predicting Students Performance?

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