The Sentiment Analysis Model of Services Providers’ Feedback

Shakhovska, Khrystyna; Shakhovska, Nataliya; Veselý, Peter

doi:10.3390/electronics9111922

Cited by 13 publications

(11 citation statements)

References 21 publications

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“…Due to its capacity to function effectively with high-dimensional data, like text categorization, the multi-class classification with the RF (Random Forest) approach has the greatest accuracy of the classification task. Shakhovska et al [12] proposed a hybrid machine learning based-method, which combined rule-based algorithms, Support Vector Machine, XGBoost, and logistic regression. The Ukrainian language was used for their work and they collected 32,007 pieces of feedback from Google Maps and divided the dataset into testing (20%) and training (80%).…”

Section: Text Sentiment Analysismentioning

confidence: 99%

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Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

2022

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Sentiment Analysis (SA) is a technique to study people’s attitudes related to textual data generated from sources like Twitter. This study suggested a powerful and effective technique that can tackle the large contents and can specifically examine the attitudes, sentiments, and fake news of “E-learning”, which is considered a big challenge, as online textual data related to the education sector is considered of great importance. On the other hand, fake news and misinformation related to COVID-19 have confused parents, students, and teachers. An efficient detection approach should be used to gather more precise information in order to identify COVID-19 disinformation. Tweet records (people’s opinions) have gained significant attention worldwide for understanding the behaviors of people’s attitudes. SA of the COVID-19 education sector still does not provide a clear picture of the information available in these tweets, especially if this misinformation and fake news affect the field of E-learning. This study has proposed denoising AutoEncoder to eliminate noise in information, the attentional mechanism for a fusion of features as parts where a fusion of multi-level features and ELM-AE with LSTM is applied for the task of SA classification. Experiments show that our suggested approach obtains a higher F1-score value of 0.945, compared with different state-of-the-art approaches, with various sizes of testing and training datasets. Based on our knowledge, the proposed model can learn from unified features set to obtain good performance, better results than one that can be learned from the subset of features.

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Section: Text Sentiment Analysismentioning

confidence: 99%

“…Sentence Score = words in sentense words in the longest sentence (12) Electronics 2022, 11, x FOR PEER REVIEW 11 of 21…”

Section: Lexicon Buildingmentioning

confidence: 99%

Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

2022

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“…The LR algorithm is applied to classify individuals in categories according to logistic function. There are many instances where a perfect graph that fits all the data points is not apparent [37]. A learn textual sentiment classifier, denoted as y = f(x) can be obtained from a training set, denoted as D ={(x1, y1), …, (xi, yi)}.…”

Section: ) Logistic Regression (Lr)mentioning

confidence: 99%

“…SVM finds the optimal linear separator between data points with a maximum margin that allows positive values greater than the margin and negative values less than the margin [37,38]. This method is called quadratic programming optimization.…”

Section: ) Support Vector Machines (Svm)mentioning

confidence: 99%

Automatically Correcting Noisy Labels for Improving Quality of Training Set in Domain-specific Sentiment Classification

Khamket

Polpinij

2022

Curr. Appl. Sci. Technol.

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Classification model performance can be degraded by label noise in the training set. The sentiment classification domain also struggles with this issue, whereby customer reviews can be mislabeled. Some customers give a rating score for a product or service that is inconsistent with the review content. If business owners are only interested in the overall rating picture that includes mislabeling, this can lead to erroneous business decisions. Therefore, this issue became the main challenge of this study. If we assume that customer reviews with noisy labels in the training data are validated and corrected before the learning process, then the training set can generate a predictive model that returns a better result for the sentiment analysis or classification process. Therefore, we proposed a mechanism, called polarity label analyzer, to improve the quality of a training set with noisy labels before the learning process. The proposed polarity label analyzer was used to assign the polarity class of each sentence in a customer review, and then polarity class of that customer review was concluded by voting. In our experiment, datasets were downloaded from TripAdvisor and two linguistic experts helped to assign the correct labels of customer reviews as the ground truth. Sentiment classifiers were developed using the k-NN, Logistic Regression, XGBoost, Linear SVM and CNN algorithms. After comparing the results of the sentiment classifiers without training set improvement and the results with training set improvement, our proposed method improved the average scores of F1 and accuracy by 20.59%.

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“…Some Sentiment Analysis software applications [2] Due to lack of specialized Sentiment Reasoning software applications so far, some users utilized available Topic Visualization methods to track evolution of topics and visually correlate curves of Topics Over Time with sentiment trends. For instance, Yin et al [3] attempted to interpret changes of public sentiment towards Covid-19 on Twitter.…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media

Alattar

Shaalan

2021

IEEE Access

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Sentiment Analysis tools allow decision-makers to monitor changes of opinions on social media towards entities, events, products, solutions, and services. These tools provide dashboards for tracking positive, negative, and neutral sentiments for platforms like Twitter where millions of users express their opinions on various topics. However, so far, these tools do not automatically extract reasons for sentiment variations, and that makes it difficult to conclude necessary actions by decision-makers. In this paper, we first compare performance of various Sentiment Analysis classifiers for short texts to select the top performer. Then we present a Filtered-LDA framework that significantly outperformed existing methods of interpreting sentiment variations on Twitter. The framework utilizes cascaded LDA Models with multiple settings of hyperparameters to capture candidate reasons that cause sentiment changes. Then it applies a filter to remove tweets that discuss old topics, followed by a Topic Model with a high Coherence Score to extract Emerging Topics that are interpretable by a human. Finally, a novel Twitter's sentiment reasoning dashboard is introduced to display the most representative tweet for each candidate reason.

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The Sentiment Analysis Model of Services Providers’ Feedback

Cited by 13 publications

References 21 publications

Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

Automatically Correcting Noisy Labels for Improving Quality of Training Set in Domain-specific Sentiment Classification

Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media

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