Scientific Text Sentiment Analysis using Machine Learning Techniques

Raza, Hassan; Faizan, Mohd; Hamza, Ahsan; Mushtaq, Ahmed; Akhtar, Naeem

doi:10.14569/ijacsa.2019.0101222

Cited by 41 publications

(24 citation statements)

References 65 publications

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“…Machine learning techniques like Naïve Bayes and OneR have been used for sentiment analysis [10]. Another similar paper also uses six different machine learning algorithms, including Naïve-Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), and Random Forest (RF) for sentiment analysis [11]. The forthcoming distribution of COVID-19 vaccines implies an urgent need to track and try to better understand public opinion on an ongoing basis to establish baseline levels of vaccine confidence and to detect early warnings of confidence loss [12].…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Sentiment Analysis of COVID-19 Vaccination from Survey Responses in Bangladesh.

Chowdhury

Das

Saha

et al. 2021

Preprint

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Objectives: The COVID-19 pandemic is among the most serious global threats, and it is still a significant concern. The people of Bangladesh are undergoing one of the world's largest vaccination drive. With the recent launch and introduction of the COVID-19 vaccines, many of us are curious about the general opinion or view of the vaccine. While the vaccine has ignited new hope in the battle against COVID-19, it has also sparked militant anti-vaccine campaigns, so the need to analyze public opinion on the COVID-19 vaccine has emerged. Methods: Traditional machine learning methods were used to obtain a benchmark result for the experiment. The recurrent neural network (RNN) algorithm was used next. Several different types of recurrent neural networks were used, including simple RNNs, Gated Recurrent Units (GRUs), and LSTMs. Finally, to achieve a more optimal result, small BERT models (Bidirectional Encoder Representations from Transformers) were used. Results: Upon study and testing on several models and methods, it can be seen that BERT model was the most accurate of the bunch, which was 84%. On the other hand, Naive Bayes was able to obtain an accuracy of 81%. Naive Bayes and BERT produced similar results in F1- Score, but the performance of Naive Bayes can improve as the dataset size grows. Conclusion: Knowing about public opinions on the COVID-19 vaccine is critical, and action must be taken to ensure that everybody understands the value of vaccination and that everybody receives the COVID-19 vaccine. Vaccination may help to develop immunity, which lowers the likelihood of contracting the disease and its consequences.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Sentiment Analysis of COVID-19 Vaccination from Survey Responses in Bangladesh.

Chowdhury

Das

Saha

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In terms of predictive accuracy, compared to a keyword-based approach. People who use the internet to make their assumptions on various topics up to this article it depicts a framework for examining sentiments on Twitter Sorts tweets according to their year, positive or negative Party level tweets [29]. Managed to learn workbooks, For example, we often type incorrectly for tweets "but, and conditional, for example," if ", because of their unusual semantic traits.…”

Section: Related Workmentioning

confidence: 99%

A Survey: Sentimental Analysis on Product Reviews Using (MLT) Machine Learning Techniques and Approaches

2021

IJATCSE

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Social media sites are used today for the development of different types and nature of customers those use such benefits which are often shared by people on social media symbolic or textual opinions, ideas, and feelings. This attitude and orientation draw attention to research and analyze sentiments through online data about customer interest. Therefore, the sentimental analysis idea is proposed. This is among the various uses of Natural Language Processing (NLP) and Machine Learning Analysis (MLA) is very common. The main task of sentimental analysis is the classification of sentiments automatically into three categories that are positive, negative and neutral. Many classification researches are conducted over the years to know the exact feelings and situations of sentimental emotions of people. Classification, fuzzy and clustering, is used. To know the sentiment analysis of the people’s accurate feeling and situation, many times over the years classification research was conducted in past. The accuracy of classification is finding more in Fuzzy based. Fuzzy based classification finds more accurate and for comparative study execution Classical Text Classifications Model is used. In comparative performance, this study shows the possibility of implementing the proposed method able to provide more accurate results when it comes in comparison with conventional classifiers. In this article we have discussed different researchers worked on the method of sentiment analysis and classification. This article also shows the importance of extracting comments and analyze sentiments

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“…We implemented various features for data classification including N-Grams [16] [17], Stop Words Removal [17], Lemmatization [17], Tokenization [17], and Case Normalization to clean down the data.…”

Section: B Features' Applicationmentioning

confidence: 99%

“…To determine the accuracy of a classification we have preferred to use Accuracy score [17], and F-Score [17] evaluation metrics. Table III represents the evaluation scores of both scientific and non-scientific data sets" classification using the F-Score and Accuracy score.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…To analyze the opinion, perception, mindset, and experience of the user is known as sentiment analysis. Judging the sentiments of citing paper" writer about cited paper is termed as sentiment analysis [17]. From the literature work, it has been identified that no work has been done on the problem of evaluating the annotation complexity of both scientific as well as non-scientific text related articles.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Scientific VS Non-Scientific Citation Annotational Complexity Analysis using Machine Learning Classifiers

Raza¹,

Faizan²,

Akhtar³

et al. 2020

IJACSA

Self Cite

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This paper evaluates the citation sentences' annotation complexity of both scientific as well as non-scientific text related articles to find out major complexity reasons by performing sentiment analysis of scientific and non-scientific domain articles using our own developed corpora of these domains separately. For this research, we selected different data sources to prepare our corpora in order to perform sentimental analysis. After that, we have performed a manual annotation procedure to assign polarities using our defined annotation guidelines. We developed a classification system to check the quality of annotation work for both domains. From results, we have found that the scientific domain gave us more accurate results than the non-scientific domain. We have also explored the reasons for less accurate results and concluded that non-scientific text especially linguistics is of complex nature that leads to poor understanding and incorrect annotation.

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Scientific Text Sentiment Analysis using Machine Learning Techniques

Cited by 41 publications

References 65 publications

WITHDRAWN: Sentiment Analysis of COVID-19 Vaccination from Survey Responses in Bangladesh.

WITHDRAWN: Sentiment Analysis of COVID-19 Vaccination from Survey Responses in Bangladesh.

A Survey: Sentimental Analysis on Product Reviews Using (MLT) Machine Learning Techniques and Approaches

Scientific VS Non-Scientific Citation Annotational Complexity Analysis using Machine Learning Classifiers

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