Sentiment analysis and spam filtering using the YAC2 clustering algorithm with transferability

Ghiassi, M.; Lee, Sean; Gaikwad, Swati Ramesh

doi:10.1016/j.cie.2022.107959

Cited by 16 publications

(9 citation statements)

References 14 publications

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“…According to their findings LR outperforms other models in terms of accuracy (over 70%). Ghiassi, M., et al [17] have performed user opinion analysis on datasets from Starbucks, Verizon, and Southwest Airlines. They employ unsupervised learning classifiers such as Yet Another Clustering and KNN.…”

Section: Review Of Machine Learning Techniques In User Opinion Analysismentioning

confidence: 99%

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Classification of Twitter COVID Tweets Using Deep CNN-SLSTM Technique

2022

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A wide range of industries, including company management, travel and tourism management, education management (E-Learning), the medical field, government agencies, telecommunications management, and private firms, can benefit from user opinion analysis. Many classifiers for deep learning and machine learning are being created to automate sentiment analysis. For sentiment analysis, real-time tweets are initially gathered from various social media platforms like Twitter and Facebook as well as numerous review websites like Amazon and Flip-kart. In this research, a real-time dataset is created using the Twitter API and is used to gather tweets about COVIDbased keywords between

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Section: Review Of Machine Learning Techniques In User Opinion Analysismentioning

confidence: 99%

“…Precision specifies the proportion of true COVID tweets from the total positive tweet samples in a dataset. It is calculated by equation(17) Recall specifies the proportion of true positive rate of COVID tweets samples. It is calculated by equation(18).…”

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confidence: 99%

Classification of Twitter COVID Tweets Using Deep CNN-SLSTM Technique

2022

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“…On the one hand, text categorization tasks are usually based on large amounts of annotated data, no matter whether they are supervised learning or self-supervised learning methods. Ghiassi et al [ 18 ] present an integrated solution that combines a new clustering algorithm, with a domain transferrable feature engineering approach for Twitter sentiment analysis and spam filtering of YouTube comments. Kim et al [ 19 ] propose a question-answer method to automatically provide users with infrastructure damage information from textual data.…”

Section: Related Workmentioning

confidence: 99%

A Multimodel-Based Deep Learning Framework for Short Text Multiclass Classification with the Imbalanced and Extremely Small Data Set

Tong¹,

Wang²,

Rui³

2022

Computational Intelligence and Neuroscience

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Text classification plays an important role in many practical applications. In the real world, there are extremely small datasets. Most existing methods adopt pretrained neural network models to handle this kind of dataset. However, these methods are either difficult to deploy on mobile devices because of their large output size or cannot fully extract the deep semantic information between phrases and clauses. This paper proposes a multimodel-based deep learning framework for short-text multiclass classification with an imbalanced and extremely small dataset. Our framework mainly includes five layers: the encoder layer, the word-level LSTM network layer, the sentence-level LSTM network layer, the max-pooling layer, and the SoftMax layer. The encoder layer uses DistilBERT to obtain context-sensitive dynamic word vectors that are difficult to represent in traditional feature engineering methods. Since the transformer part of this layer is distilled, our framework is compressed. Then, we use the next two layers to extract deep semantic information. The output of the encoder layer is sent to a bidirectional LSTM network, and the feature matrix is extracted hierarchically through the LSTM at the word and sentence level to obtain the fine-grained semantic representation. After that, the max-pooling layer converts the feature matrix into a lower-dimensional matrix, preserving only the obvious features. Finally, the feature matrix is taken as the input of a fully connected SoftMax layer, which contains a function that can convert the predicted linear vector into the output value as the probability of the text in each classification. Extensive experiments on two public benchmarks demonstrate the effectiveness of our proposed approach on an extremely small dataset. It retains the state-of-the-art baseline performance in terms of precision, recall, accuracy, and F1 score, and through the model size, training time, and convergence epoch, we can conclude that our method can be deployed faster and lighter on mobile devices.

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“…Previous studies proposed some mechanisms for detecting spam reviews and for predicting and ranking helpful reviews [12,13] on various opinion websites. Research [14] on spam detection has incorporated the technique of natural language processing (NLP) to process reviews. Since it is difficult to perform sentiment analysis, it is difficult to distinguish between spam and genuine reviews through natural language processing, and the accuracy of spam detection is not as high as expected.…”

Section: Introductionmentioning

confidence: 99%

Trustworthiness of Review Opinions on the Internet for 3C Commodities

Hsieh,

Lu,

Yang

2024

Electronics

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The rapid development of the internet has resulted in rapid e-business growth, with online malls attracting many shoppers due to the privacy and convenience they offer. Like traditional malls, online malls can provide photos, specifications, prices, etc. However, consumers cannot touch the products in reality, which creates risks for the purchase. To date, there has been no research focusing on topic-specific search engines for 3C product reviews based on the trustworthiness of the reviews. This study is the first to sort the reviews of electronic products according to the degree of trust, by analyzing the characteristics of the reviews and the reviewers. This study proposes the criteria for features of the reviews and reviewers to consider to evaluate the trustworthiness of the reviews; builds a search engine to collect the product reviews scattered in opinion websites; and sorts the results by trustworthiness to provide a reliable e-commerce experience. To demonstrate the effectiveness of the proposed method, we conducted a set of experiments, and we adopted the Spearman’s rank correlation coefficient to evaluate the similarity between our method and experts’ opinions. The experimental results showed a high correlation coefficient with the opinions of experts, demonstrating that our method is effective at finding trustworthy reviews on the internet.

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Sentiment analysis and spam filtering using the YAC2 clustering algorithm with transferability

Cited by 16 publications

References 14 publications

Classification of Twitter COVID Tweets Using Deep CNN-SLSTM Technique

Classification of Twitter COVID Tweets Using Deep CNN-SLSTM Technique

A Multimodel-Based Deep Learning Framework for Short Text Multiclass Classification with the Imbalanced and Extremely Small Data Set

Trustworthiness of Review Opinions on the Internet for 3C Commodities

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