Sentiment Analysis of Sina Weibo Users Under the Impact of Super Typhoon Lekima Using Natural Language Processing Tools: A Multi-Tags Case Study

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

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A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽

10.1177/23328584211028615 ◽

2021 ◽

Vol 7 ◽

pp. 233285842110286

Author(s):

Kylie L. Anglin ◽

Vivian C. Wong ◽

Arielle Boguslav

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Semantic Similarity ◽

Language Processing ◽

Intervention Implementation ◽

Proof Of Concept ◽

Coaching Intervention ◽

Processing Techniques ◽

Teacher Coaching ◽

The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

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Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01352-2 ◽

2020 ◽

Vol 20 (S11) ◽

Cited By ~ 1

Author(s):

Jacqueline Peng ◽

Mengge Zhao ◽

James Havrilla ◽

Cong Liu ◽

Chunhua Weng ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Clinical Manifestations ◽

Predictive Performance ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Research Articles

Abstract Background Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, cTAKES, and MetaMap are among the most widely used tools to extract biomedical concept entities. However, their performance in extracting disease-specific terminology from literature has not been compared extensively, especially for complex neuropsychiatric disorders with a diverse set of phenotypic and clinical manifestations. Methods We comparatively evaluated these NLP tools using autism spectrum disorder (ASD) as a case study. We collected 827 ASD-related terms based on previous literature as the benchmark list for performance evaluation. Then, we applied CLAMP, cTAKES, and MetaMap on 544 full-text articles and 20,408 abstracts from PubMed to extract ASD-related terms. We evaluated the predictive performance using precision, recall, and F1 score. Results We found that CLAMP has the best performance in terms of F1 score followed by cTAKES and then MetaMap. Our results show that CLAMP has much higher precision than cTAKES and MetaMap, while cTAKES and MetaMap have higher recall than CLAMP. Conclusion The analysis protocols used in this study can be applied to other neuropsychiatric or neurodevelopmental disorders that lack well-defined terminology sets to describe their phenotypic presentations.

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Coarse-Grained Sentiment Analysis Berbasis Natural Language Processing – Ulasan Hotel

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI) ◽

10.22146/jnteti.v10i1.548 ◽

2021 ◽

Vol 10 (1) ◽

pp. 41-48

Author(s):

Warnia Nengsih ◽

M. Mahrus Zein ◽

Nazifa Hayati

Keyword(s):

Natural Language Processing ◽

Random Forest ◽

Natural Language ◽

Receiver Operating Characteristic ◽

Sentiment Analysis ◽

Language Processing ◽

Operating Characteristic ◽

Coarse Grained ◽

Receiver Operating

Sentiment analysis adalah metode untuk memperoleh data dari berbagai platform yang tersedia di internet. Kemajuan teknologi memungkinkan mesin untuk mengenali suatu istilah yang dianggap sebagai opini positif maupun sebaliknya. Data-data dan opini tersebut berperan penting sebagai umpan balik produk, layanan, dan topik lainnya. Tanpa perlu memperoleh opini secara langsung dari masyarakat, pihak penyedia telah mendapatkan evaluasi yang penting guna mengembangkan diri. Bisnis perhotelan merupakan bidang yang terkait dengan jasa memberikan layanan pada pelanggan. Indikator keberlangsungan bisnis ini juga bergantung pada umpan balik pelanggannya dan dijadikan sebagai acuan untuk pengambilan kebijakan strategis. Teknik sentiment analysis berbasis Natural Language Processing dapat mengatasi permasalahan tersebut. Pada makalah ini prediksi dilakukan menggunakan classifier Random Forest (RF), sementara untuk merangkum kualitas classifier, digunakan kurva Receiver Operating Characteristic (ROC). Kurva ROC berupa grafik yang baik untuk merangkum kualitas classifier. Semakin tinggi kurva berada di atas garis diagonal, semakin baik prediksinya, dengan nilai kurva ROC yang diperoleh sebesar 0,90. Terlihat hasil ulasan terhadap opini pelanggan terhadap jasa dan pelayanan yang diberikan oleh hotel untuk kategori positif lebih banyak daripada kategori negatif. Polaritas dari ulasan diperoleh 68% ulasan pelanggan berada pada area positif dan 32% berada pada area negatif.

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Natural Language Processing (NLP) and Its Impact across Industries – Unlocking the True Potential of Digital Healthcare (A Case Study Approach)

Journal of Pharmaceutical Research International ◽

10.9734/jpri/2021/v33i35b31906 ◽

2021 ◽

pp. 86-98

Author(s):

Sourajit Roy ◽

Pankaj Pathak ◽

S. Nithya

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Computational Models ◽

M2m Communication ◽

Digital Revolution ◽

Language Complexity ◽

Study Approach ◽

Data Variability

During the advent of the 21st century, technical breakthroughs and developments took place. Natural Language Processing or NLP is one of their promising disciplines that has been increasingly dynamic via groundbreaking findings on most computer networks. Because of the digital revolution the amounts of data generated by M2M communication across devices and platforms such as Amazon Alexa, Apple Siri, Microsoft Cortana, etc. were significantly increased. This causes a great deal of unstructured data to be processed that does not fit in with standard computational models. In addition, the increasing problems of language complexity, data variability and voice ambiguity make implementing models increasingly harder. The current study provides an overview of the potential and breadth of the NLP market and its acceptance in industry-wide, in particular after Covid-19. It also gives a macroscopic picture of progress in natural language processing research, development and implementation.

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Sentiment Analysis on Twitter Airline Data

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35807 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 3767-3770

Author(s):

Kirti Jain

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Sentiment Analysis ◽

Language Processing ◽

Learning Task ◽

Model Based ◽

Sentiment Mining ◽

General Opinion

Sentiment analysis, also known as sentiment mining, is a submachine learning task where we want to determine the overall sentiment of a particular document. With machine learning and natural language processing (NLP), we can extract the information of a text and try to classify it as positive, neutral, or negative according to its polarity. In this project, We are trying to classify Twitter tweets into positive, negative, and neutral sentiments by building a model based on probabilities. Twitter is a blogging website where people can quickly and spontaneously share their feelings by sending tweets limited to 140 characters. Because of its use of Twitter, it is a perfect source of data to get the latest general opinion on anything.

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EMOSIS Sentiment Analysis on Tweets with Emotion and Intensity Level Recognition Considering Ending Punctuation Marks

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d4518.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 10289-10293

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Emotion Recognition ◽

Sentiment Analysis ◽

Language Processing ◽

Significant Role ◽

Language Model ◽

Intensity Level ◽

Processing Stage ◽

Overall Performance

Sentiment Analysis is a tool used for determining the Polarity or Emotion of a Sentence. It is a field of Natural Language Processing which focuses on the study of opinions. In this study, the researchers solved one key challenge in Sentiment Analysis, which is to consider the Ending Punctuation Marks present in a sentence. Ending punctuation marks plays a significant role in Emotion Recognition and Intensity Level Recognition. The research made used of tweets expressing opinions about Philippine President Rodrigo Duterte. These downloaded tweets served as the inputs. It was initially subjected to pre-processing stage to be able to prepare the sentences for processing. A Language Model was created to serve as the classifier for determining the scores of the tweets. The scores give the polarity of the sentence. Accuracy is very important in sentiment analysis. To increase the chance of correctly identifying the polarity of the tweets, the input undergone Intensity Level Recognition which determines the intensifiers and negations within the sentences. The system was evaluated with overall performance of 80.27%.

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