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Ghabayen, Ayman S., and Basem H. Ahmed. "Polarity Analysis of Customer Reviews Based on Part-of-Speech Subcategory." Journal of Intelligent Systems 29, no. 1 (2019): 1535–44. http://dx.doi.org/10.1515/jisys-2018-0356.
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Abstract Nowadays, sentiment analysis is a method used to analyze the sentiment of the feedback given by a user in an online document, such as a blog, comment, and review, and classifies it as negative, positive, or neutral. The classification process relies upon the analysis of the polarity features of the natural language text given by users. Polarity analysis has been an important subtask in sentiment analysis; however, detecting correct polarity has been a major issue. Different researchers have utilized different polarity features, such as standard part-of-speech (POS) tags such as adjectives, adverbs, verbs, and nouns. However, there seems to be a lack of research focusing on the subcategories of these tags. The aim of this research was to propose a method that better recognizes the polarity of natural language text by utilizing different polarity features using the standard POS category and the subcategory combinations in order to explore the specific polarity of text. Several experiments were conducted to examine and compare the efficacies of the proposed method in terms of F-measure, recall, and precision using an Amazon dataset. The results showed that JJ + NN + VB + RB + VBP + RP, which is a POS subcategory combination, obtained better accuracy compared to the baseline approaches by 4.4% in terms of F-measure.
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Saeed, Sadia, Tehseen Zahra, and Asim Ali Fayyaz. "Sentiment Analysis of Imran Khan’s Tweets." Volume 36, Issue 3 36, no. 3 (2021): 473–94. http://dx.doi.org/10.33824/pjpr.2021.36.3.26.
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In the recent past, sentiment analysis has been an area of interests of psychologists, sociologists, neurologists, computer scientists, and linguists including corpus linguists and computational linguists. Interdisciplinary approaches to researching various issues especially the analysis of social media websites such as Facebook, Twitter, and Instagram are becoming popular nowadays. The availability of data on social media has made it easier to analyse the opinion or sentiments of its users. Analysis of these sentiments could reveal the face of users and it could help in various decision-making processes. Sentiment analysis is a system of knowing polarity (positive, negative, and neutral) in discourse. Moreover, sentiments can enable and disable certain functions of discourse and can divert the attention of the audience from important to a less important issue or otherwise, hence, there is a need to analyse the sentiments. In this research, sentiments (Polarity) of Imran Khan’s tweets are analysed with the help of R studio. Data for this study is collected from Imran Khan’s one-year’s tweets, tweeted from 1st January 2018 to 20th November 2018. Later we saved the data in. csv files. The results of the polarity check revealed that he has used all three types of sentiments that is positive, negative, and neutral. However, he mostly used neutral or free polarity items (FPIs) that is 67.41% in his tweets. Among positive and negative polarity items the number of negative polarity items (NPIs) is higher that is 23.21% as compared to positive polarity items (PPIs) which are only 9.40%. The manual analysis of results revealed that only software is not enough and there is a need to check the accuracy of the results manually. The use of negative polarity/negative face reveals that he tries to be independent and autonomous in his decisions (Goffman, 1967). The use of positive polarity items shows he tries to show his positive face to others. Moreover, sentiment analysis demonstrates the presence of themes propagated through the use of various lexical items.
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Sarkar, Kamal. "Sentiment Polarity Detection in Bengali Tweets Using Deep Convolutional Neural Networks." Journal of Intelligent Systems 28, no. 3 (2019): 377–86. http://dx.doi.org/10.1515/jisys-2017-0418.
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Abstract Sentiment polarity detection is one of the most popular sentiment analysis tasks. Sentiment polarity detection in tweets is a more difficult task than sentiment polarity detection in review documents, because tweets are relatively short and they contain limited contextual information. Although the amount of blog posts, tweets and comments in Indian languages is rapidly increasing on the web, research on sentiment analysis in Indian languages is at the early stage. In this paper, we present an approach that classifies the sentiment polarity of Bengali tweets using deep neural networks which consist of one convolutional layer, one hidden layer and one output layer, which is a soft-max layer. Our proposed approach has been tested on the Bengali tweet dataset released for Sentiment Analysis in Indian Languages contest 2015. We have compared the performance of our proposed convolutional neural networks (CNN)-based model with a sentiment polarity detection model that uses deep belief networks (DBN). Our experiments reveal that the performance of our proposed CNN-based system is better than our implemented DBN-based system and some existing Bengali sentiment polarity detection systems.
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HONDA, KOHEI, and NOBUKO YOSHIDA. "Noninterference through flow analysis." Journal of Functional Programming 15, no. 2 (2005): 293–349. http://dx.doi.org/10.1017/s0956796804005477.
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This paper proposes new syntactic inference rules which can directly extract information flow in a given typed process in the π-calculus. In the flow analysis, a flow in a process is captured as a chain of possible interactions which transform differences in behaviours from one part of its interface to another part of its interface. Polarity in types plays a fundamental role in the analysis, which is elucidated via examples. We show that this inductive flow analysis can be used for giving simple proofs of noninterference in the secrecy analyses for the π-calculus with linear/affine typing, including its concurrent, stateful extensions.
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Hamilton, R. S., R. M. Parton, R. A. Oliveira, et al. "ParticleStats: open source software for the analysis of particle motility and cytoskeletal polarity." Nucleic Acids Research 38, Web Server (2010): W641—W646. http://dx.doi.org/10.1093/nar/gkq542.
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Yergesh, Banu, Gulmira Bekmanova, and Altynbek Sharipbay. "Sentiment analysis of Kazakh text and their polarity." Web Intelligence 17, no. 1 (2019): 9–15. http://dx.doi.org/10.3233/web-190396.
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J., Sangeetha, and Dr Kumaran U. "Comparison of Sentiment Analysis on Online Product Reviews Using Optimised RNN-LSTM with Support Vector Machine." Webology 19, no. 1 (2022): 3883–98. http://dx.doi.org/10.14704/web/v19i1/web19256.
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Using sentiment analysis, opinion mining examines the emotional tone and polarity of text (positive, neutral, or negative), as well as the sentiment polarity of text. With the rise of online information, sentiment analysis of customer evaluations has become a hot topic among machine learning researchers. Review texts for products online express a wide range of feelings and thoughts. By using natural language processing tools on the Internet, it will be possible for natural language processors to extract useful information from online reviews by performing sentiment analysis. It assigns polarity to a positive or negative entity or item. From the product reviews collected on Amazon, we conduct a Sentiment Analysis. As a result of asymmetrical weighting, we feed our feature words to support vector machine classifiers as well as Recurrent Neural Networks-Long Short Term Memory (RNN-LSTM)-optimised methods for determining the sentiment direction of reviews.
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A. Hnaif, Adnan, Emran Kanan, and Tarek Kanan. "Sentiment Analysis for Arabic Social Media News Polarity." Intelligent Automation & Soft Computing 28, no. 1 (2021): 107–19. http://dx.doi.org/10.32604/iasc.2021.015939.
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Zeng, Lin Suo, and Zhe Wu. "The Finite Element Analysis of the Large-Scale Converter Transformer Valve Side of the Electric Field." Applied Mechanics and Materials 325-326 (June 2013): 476–79. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.476.
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This article is based on finite element theory and use ANSYS simulation software to establish electric field calculation model of converter transformer for a ±800kV and make electric field calculation and analysis for valve winding. Converter transformer valve winding contour distribution of electric field have completed in the AC, DC and polarity reversal voltage.
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Ji, Jiaying. "Chinese Language Literature Emotion Analysis Model Based on Unbalanced K-Nearest Neighbor Classification Method." Scientific Programming 2022 (March 3, 2022): 1–11. http://dx.doi.org/10.1155/2022/7781741.
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The Chinese language is a nation’s symbol, the accumulation of a country’s magnificent culture, and the pearl precipitated in the long history’s washing. The Chinese language is rich and complex, and there are still many topics and issues that merit repeated exchanges and discussions in academic circles. This study proposes a classification method of emotion polarity based on reliability analysis in order to identify the tendency of literary emotion in Chinese language. Support vector machine (SVM), class center, and KNN (K-nearest neighbor) are included in the combined classifier, which effectively improves the accuracy and efficiency of emotion polarity classification. A Chinese literary emotion analysis model based on the method of UKNNC (unbalanced K-nearest neighbor classification) is proposed at the same time by analysing the characteristics of text structure and emotion expression. The experimental results show that, when compared to traditional machine methods, the UKNNC method can analyse text sentiment in fine-grained and multilevel ways, while also improving the accuracy of Chinese literary sentiment analysis.
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Dr.Thakur, Ashwini, and Priya R. Sheth Dr. "A Prospective Non-Randomized Experimental Study to Compare Results of BTPB and Polarity Analysis Software in Cases of Osteoarthritis using Womac Index." Journal of Harbin Engineering University Volume 44 No.8, no. 8 (2023): 1607–23. https://doi.org/10.5281/zenodo.8424568.
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Abstract: Introduction: Osteoarthritis is a long-term chronic disease characterized by deterioration of cartilage in joints which results in bones rubbing together and creating stiffness, pain, and impaired movements. The disease most commonly affects the joints in the knees, hands, and feet and is relatively common in the shoulder and hip joints. While osteoarthritis is related to aging, it is associated with a variety of risk factors including obesity, lack of exercise, genetic predisposition, bone density, occupational injury, trauma, and gender. Primary osteoarthritis is a condition in which the protein makeup of cartilage decreases with age, repetitive use of joints over the years causes damage to the cartilage that leads to joint pain and swelling. Secondary osteoarthritis is mostly caused by another underlying disease for ex: osteoarthritis to obesity, repeated Trauma or surgery to the abnormal Joints at birth, gout, Rheumatoid arthritis, Diabetes mellitus & other hormonal disorders. This study aims to show the utility of homeopathic medicines on patients suffering from Osteoarthritis, and to compare the results of Polarity Analysis Software and BTPB in the management of Osteoarthritis, Using WOMAC score for before and after intervention Comparison. Materials and method 30 patients, in the age group of 30 to 70 years, diagnosed with Osteoarthritis, were selected to conduct this study. Out of the 30 patients, 57% of patients were females and 43% were males in the study. The changes were evaluated before and after treatment. Response to treatment was determined by WOMAC Index which includes pain, stiffness and ADL, before and after the treatment. After careful examination, study, and repertorization with the help of Boenninghausen therapeutic pocket book and polarity analysis each of the 15 cases was analyzed by using both the repertories, and the similimum was prescribed to the patients. Results: In this study, a total of 32 Patients have registered out of which 30 patients were able to complete their treatment and 2 cases were dropout, the age group involved in this study is varying from 35-70 years age old 1608 Journal of Harbin Engineering University ISSN: 1006-7043 Vol 44 No. 8 August 2023 people Among 30 Patients of 60% of patients had both their knees affected, 20% of had their left knee affected and 20% of patients had issues with the right knee. Patients the Distribution of patients according to the Medicine Prescribed for the treatment of knee osteoarthritis. Belladonna, Sulphur, and Kali Carb each of this medicine were prescribed to 10% of patients under study. WOMAC Index was used as the assessment tool, before and after intervention scores were statistically analysed; the pain, stiffness, and functional limitation subcategories were statistically assessed using the ‘t-test before intervention and after the intervention. Here average “WOMAC Score Difference” is compared for both Repertory BTPB and Polarity Analysis. When BTPB Repertory is used, the average “WOMAC score difference” is 28.67 + 6.03(mean± sd) before and after the intervention of treatment for knee joint pain. When Polarity Analysis is used, the average “WOMAC score difference ”is 35.53+ 7.71 (mean± sd) before and after the intervention of treatment for knee joint pain. To test the hypothesis: The average “WOMAC score difference“, based on BTPB and Polarity analysis treatment is the same, a two-sample t-test is used. The t-statistic value is -2.72 and the p-value of 0.01* is significant. Conclusion: The medicines, which were selected and prescribed on the basis of chief complaint, after the repertorization from BTPB and Polarity Analysis software, showed significant result in the treatment of osteoarthritis. Homeopathic medicines obtained after repertorizing from Polarity Analysis software are effective in the treatment of osteoarthritis. The results are supportive to open new paths for future studies on Osteoarthritis and homeopathic treatment. As the sample size was small further studies with larger sample sizes are needed.
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Chai, Jing Fu, Qiong Zou, and Wen Qing Song. "Magnetic Field Interaction of Tiny Grinding Wheel Cluster Based on Magnetorheological Effect." Advanced Materials Research 774-776 (September 2013): 1038–41. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1038.
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The tiny grinding wheel cluster based on MR effect is an ultra-precision machining tool. The structural optimization and magnetic pole characteristics of machining tool are studied in order to improve its machining performance. In this paper, the Maxwell3D of finite element analysis software is used to simulate and analyze the magnetic field interaction of tiny grinding wheel cluster based on MR effect. A polishing experiment is done, and the results show that the polishing effect in the same polarity is better than that in the opposite polarity.
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Gupta, Itisha, and Nisheeth Joshi. "Enhanced Twitter Sentiment Analysis Using Hybrid Approach and by Accounting Local Contextual Semantic." Journal of Intelligent Systems 29, no. 1 (2019): 1611–25. http://dx.doi.org/10.1515/jisys-2019-0106.
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Abstract This paper addresses the problem of Twitter sentiment analysis through a hybrid approach in which SentiWordNet (SWN)-based feature vector acts as input to the classification model Support Vector Machine. Our main focus is to handle lexical modifier negation during SWN score calculation for the improvement of classification performance. Thus, we present naive and novel shift approach in which negation acts as both sentiment-bearing word and modifier, and then we shift the score of words from SWN based on their contextual semantic, inferred from neighbouring words. Additionally, we augment negation accounting procedure with a few heuristics for handling the cases in which negation presence does not necessarily mean negation. Experimental results show that the contextual-based SWN feature vector obtained through shift polarity approach alone led to an improved Twitter sentiment analysis system that outperforms the traditional reverse polarity approach by 2–6%. We validate the effectiveness of our hybrid approach considering negation on benchmark Twitter corpus from SemEval-2013 Task 2 competition.
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Zucco, Chiara, Clarissa Paglia, Sonia Graziano, Sergio Bella, and Mario Cannataro. "Sentiment Analysis and Text Mining of Questionnaires to Support Telemonitoring Programs." Information 11, no. 12 (2020): 550. http://dx.doi.org/10.3390/info11120550.
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While several studies have shown how telemedicine and, in particular, home telemonitoring programs lead to an improvement in the patient’s quality of life, a reduction in hospitalizations, and lower healthcare costs, different variables may affect telemonitoring effectiveness and purposes. In the present paper, an integrated software system, based on Sentiment Analysis and Text Mining, to deliver, collect, and analyze questionnaire responses in telemonitoring programs is presented. The system was designed to be a complement to home telemonitoring programs with the objective of investigating the paired relationship between opinions and the adherence scores of patients and their changes through time. The novel contributions of the system are: (i) the design and software prototype for the management of online questionnaires over time; and (ii) an analysis pipeline that leverages a sentiment polarity score by using it as a numerical feature for the integration and the evaluation of open-ended questions in clinical questionnaires. The software pipeline was initially validated with a case-study application to discuss the plausibility of the existence of a directed relationship between a score representing the opinion polarity of patients about telemedicine, and their adherence score, which measures how well patients follow the telehomecare program. In this case-study, 169 online surveys sent by 38 patients enrolled in a home telemonitoring program provided by the Cystic Fibrosis Unit at the “Bambino Gesù” Children’s Hospital in Rome, Italy, were collected and analyzed. The experimental results show that, under a Granger-causality perspective, a predictive relationship may exist between the considered variables. If supported, these preliminary results may have many possible implications of practical relevance, for instance the early detection of poor adherence in patients to enable the application of personalized and targeted actions.
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Bordoloi, Monali, and Saroj Kr Biswas. "Graph based sentiment analysis using keyword rank based polarity assignment." Multimedia Tools and Applications 79, no. 47-48 (2020): 36033–62. http://dx.doi.org/10.1007/s11042-020-09289-4.
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Muppidi, Satish, Satya Keerthi Gorripati, and B. Kishore. "An approach for bibliographic citation sentiment analysis using deep learning." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 4 (2021): 353–62. http://dx.doi.org/10.3233/kes-200087.
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Sentiment analysis of scientific citations is a novel and remarkable research area. Most of the work on opinion or sentiment analysis has been suggested on social platforms such as Blogs, Twitter, and Facebook. Nevertheless, when it comes to recognizing sentiments from scientific citation papers, investigators used to face difficulties due to the implied and unseen natures of sentiments or opinions. As the citation references are reflected implicitly positive in opinion, famous ranking and indexing prototypes frequently disregard the sentiment existence while citing. Hence, in the proposed framework the paper emphasizes the issue of classifying positive and negative polarity of reference sentiments in scientific research papers. First, the paper scraps the PDF articles from arxiv.org under the computer science group consisting of articles that are comprised of ‘autism’ in their title, then the paper extracted cited references and assigns polarity scores to each cited reference. The paper uses a supervised classifier with a combination of significant feature sets and compared the performance of the models. Experimental results show that a combined CNN-LSTM deep neural network model results in 85% of accuracy while traditional models result in less accuracy.
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Santos, Anna Isabelle Gomes Pereira, André Riani Costa Perinotto, Jakson Renner Rodrigues Soares, Tiago Savi Mondo, and Priscila Cembranel. "Expressing the Experience: An Analysis of Airbnb Customer Sentiments." Tourism and Hospitality 3, no. 3 (2022): 685–705. http://dx.doi.org/10.3390/tourhosp3030042.
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There is a growing interest in research related to Airbnb, and one theme that has stood out is the analysis of the consumer experience. This study aimed to analyse the feelings expressed in the online evaluation of users on the Airbnb platform in Fortaleza, capital of Ceará, Brazil. The methodology was developed through quali-quantitative research, a documentary research procedure, and data collection regarding the accommodation offers available on the platform. A total of 2353 reviews in 2019 and 2020 related to 506 accommodation offers were analysed through manual coding with the aid of NVivo software. The results evidenced the positivity of the evaluations, and that positive comments presented fewer characters while negative evaluations presented more details. It was identified that there were differences in the percentages of positive and negative evaluations when differentiated by other factors such as gender of the user (women evaluated more positively and intensely), type of host (superhost evaluations were more positive), type of offer (for entire places, the positive polarity was lower than the private room and shared room types), and location (the positive polarity was higher in residential neighbourhoods than in tourist neighbourhoods). Methodologically, this study contributes by illustrating how a set of evaluations can be analysed and interpreted in studies on the accommodation service.
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Luo, Siyin, Youjian Gu, Xingxing Yao, and Wei Fan. "Research on Text Sentiment Analysis Based on Neural Network and Ensemble Learning." Revue d'Intelligence Artificielle 35, no. 1 (2021): 63–70. http://dx.doi.org/10.18280/ria.350107.
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In view of the fact that a single sentiment classification model may be unstable in classification, this paper attempts to propose a joint neural network and ensemble learning sentiment analysis method. After data preprocessing such as word segmentation on the text, combined with document vectorization method for feature extraction, we then use four basic classifiers including long short-term memory network, convolutional neural network, a serial model combining convolutional neural network and long short-term memory network, and support vector machine to train model, respectively. Finally, the integration is carried out by stacking ensemble learning. The experimental results show that the integrated model significantly improves the accuracy of text sentiment analysis and it can effectively predict the sentiment polarity of the text.
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Parmar, Dr Divya. "Utility of Polarity Analysis Software in Uncomplicated Urinary Tract Infections (UTI) in Women Using Uti-Siq Questionnaire." International Journal of Science, Engineering and Technology 13, no. 2 (2025): 1–10. https://doi.org/10.61463/ijset.vol.13.issue2.304.
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Thakur, Ashwini, and Dr. Priya Sheth. "AN OVERVIEW ON MANAGEMENT OF OSTEOARTHRITIS USING HOMOEOPATHY AND A BRIEF EXPLANATION OF DR. HEINER FREI'S USE OF POLARITY ANALYSIS SOFTWARE TO ACHIEVE PRECISE HOMOEOPATHIC SIMILIMUM." Journal of Nonlinear Analysis and Optimization 14, no. 1 (2024): 158–68. https://doi.org/10.5281/zenodo.10531402.
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This review provides a comprehensive overview of the efficacy of homoeopathic medicines in treating Osteoarthritis (OA). Through a meticulous examination of available literature, the article explores the causes, signs, and conventional treatments of OA from the unique perspective of homoeopathy. Homoeopathic interventions, rooted in the principle of "like cures like," are scrutinized for their ability to alleviate joint pains associated with OA. The analysis reveals promising outcomes, indicating that homoeopathy holds the potential in reducing the discomfort caused by OA and contributing to the enhancement of Activities of Daily Living (ADL). By focusing on the holistic approach of homoeopathy, which considers individual constitutional factors, this review sheds light on the mechanism of action of homoeopathic remedies in managing OA symptoms. Notably, the examination emphasizes the role of homoeopathy in not only mitigating pain but also positively influencing overall joint function. The article underscores the importance of functional independence in OA management and suggests that homoeopathy may play a valuable role in improving ADL.In conclusion, this review consolidates existing knowledge on the subject, providing valuable insights into the efficacy of homoeopathic medicines for OA treatment. While acknowledging the positive impact of homoeopathy on joint pains and ADL, it encourages further research to establish a more robust evidence base and facilitate the integration of homoeopathy into mainstream OA therapeutic strategies.An attempt is made to understand Dr. Heiner Freis's Polarity analysis software and the different studies on its efficacy
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Mokhtari, Parham. "mGlif: A software tool for manual glottal inverse filtering." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A207. http://dx.doi.org/10.1121/10.0023296.
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Despite decades of advances in automated estimation of the voice source (glottal flow) from the speech signal, manual glottal inverse filtering remains the gold standard; yet, options are limited regarding available software tools. Here, a new software application for glottal-flow estimation is introduced, with a graphical user interface designed for manual specification and tuning of inverse-filter parameters while results are updated and displayed in real-time. The user can flexibly control the signal polarity and sampling rate, the analysis frame position and duration, and the type of window and preemphasis for vocal-tract modeling. The tunable inverse-filter parameters are the frequencies and bandwidths of vocal-tract poles and zeros, and the lip-radiation coefficient. A single window displays graphical information in several panels: the speech waveform (scrollable), the selected frame, its log-power spectrum before and after preemphasis, the model spectrum including vocal-tract and lip-radiation effects, the estimated glottal-flow waveform, its derivative, and their log-power spectra. All information pertaining to an analysis may be saved at any time, ready to be reloaded for review or analysis resumption. Key data may also be exported for further use outside the application. The software will be made freely available to the research community.
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Bhargava, Rupal, Shivangi Arora, and Yashvardhan Sharma. "Neural Network-Based Architecture for Sentiment Analysis in Indian Languages." Journal of Intelligent Systems 28, no. 3 (2019): 361–75. http://dx.doi.org/10.1515/jisys-2017-0398.
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Abstract Sentiment analysis refers to determining the polarity of the opinions represented by text. The paper proposes an approach to determine the sentiments of tweets in one of the Indian languages (Hindi, Bengali, and Tamil). Thirty-nine sequential models have been created using three different neural network layers [recurrent neural networks (RNNs), long short-term memory (LSTM), convolutional neural network (CNN)] with optimum parameter settings (to avoid over-fitting and error accumulation). These sequential models have been investigated for each of the three languages. The proposed sequential models are experimented to identify how the hidden layers affect the overall performance of the approach. A comparison has also been performed with existing approaches to find out if neural networks have an added advantage over traditional machine learning techniques.
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Rosli, Roslinda, Mardina Abdullah, Nur Choiro Siregar, et al. "Student Awareness of Space Science: Rasch Model Analysis for Validity and Reliability." World Journal of Education 10, no. 3 (2020): 170. http://dx.doi.org/10.5430/wje.v10n3p170.
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Validity and reliability are crucial when conducting research to ensure the truthfulness of an instrument. This study investigated the measurement functioning of an instrument on students' awareness of space science. The instrument was administered to 206 secondary school students involved in the Sudden Ionospheric Disturbance π outreach program. Two experts evaluated the content validity of the instrument. Data were analyzed using the Winsteps 3.71.0.1 software to obtain the Rasch model analysis (RMA) on item reliability and persons' separation, item measure, item fit based on PTMEA CORR, polarity items, misfit items, unidimensionality, and a person-item map. The findings revealed that the items are valid, reliable, and appropriate to measure awareness of space science.
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Mohamed Mostafa, Ayman. "Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset." Intelligent Automation & Soft Computing 35, no. 1 (2023): 1015–34. http://dx.doi.org/10.32604/iasc.2023.028041.
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Madzlan, Noor Alhusna, Ridzwan Che Rus, Mazlina Che Mustafa, and Sopia Md Yassin. "Validity and Reliability of Survey Items in Employer Perspective Construct on the Quality of ECCE: Rasch Measurement Model Analysis." World Journal of English Language 12, no. 2 (2022): 288. http://dx.doi.org/10.5430/wjel.v12n2p288.
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This paper examines and verifies the reliability of a survey instrument on the long term impact of early childhood and childcare education (ECCE) toward human capital development. The survey consists of separate questionnaires based on four perspectives; Individual Success, Peers, Parents and Employer perspective. This study highlights the reliability of item constructs from the employer perspective questionnaire distributed in the pilot study. This instrument was developed based on 56 items, and was further categorised into three sub-constructs; 1) individual character, 2) soft skills; and 3) good citizenship. Rasch Measurement Model analysis supported by Winsteps software version 3.73 was utilised to examine reliability of item and person, polarity of item and suitability of item. Findings from analysis of reliability of item indicated that Individual Character subconstruct showed a good level of reliability, whilst Soft Skills and Good Citizenship subconstructs showed reliability below par. Further analysis on polarity of item indicated all items scored positive values to measure the construct. While analysis on item fit revealed that a total of 6 items from the three subconstructs were discarded as they did not meet the criteria specified in the Rasch Model.
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Huang, Weihao, Shaohua Cai, Haoran Li, and Qianhua Cai. "Structure Graph Refined Information Propagate Network for Aspect-Based Sentiment Analysis." International Journal of Data Warehousing and Mining 19, no. 1 (2023): 1–20. http://dx.doi.org/10.4018/ijdwm.321107.
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The main task of aspect-based sentiment analysis is to determine the sentiment polarity of a given aspect in the sentence. A major issue lies in identifying the aspect sentiment is to establish the relationship between the aspect and its opinion words. The application of syntactic dependency trees is one such resolution. However, the widely-used dependency parsers still have challenges in obtaining a solid sentiment classification result. In this work, an information propagation graph convolutional network based on syntactic structure optimization is proposed on the task of ABSA. To further complement the syntactic information, the semantic information is incorporated to learn the representations using graph information propagation mechanism. In addition, the effects of syntactic and semantic information are adapted via feature separation. Experimental results on three benchmark datasets show that the proposed model achieves satisfying performance against the state-of-the-art methods, indicating that the model can precisely build the relation between aspect and its context words.
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Jia, Fengzhen, and Chun-Chun Chen. "Emotional characteristics and time series analysis of Internet public opinion participants based on emotional feature words." International Journal of Advanced Robotic Systems 17, no. 1 (2020): 172988142090421. http://dx.doi.org/10.1177/1729881420904213.
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In recent years, with the rapid development and wide application of the Internet, it has become the main place for the generation and dissemination of public opinion. To grasp the information of network public opinion in a timely and comprehensive way can not only effectively prevent sudden network malignant events but also provide a reference for the scientific and democratic decision-making of government departments. Therefore, in view of the practical application needs, this article studies the emotional characteristics and the evolution of public opinion over time based on the emotional feature words of network public opinion participants. Firstly, the positive and negative emotional lexicon of HowNet emotional dictionary is used, and the commonly used emotional lexicon and expression symbols are added to the lexicon. At the same time, the polarity annotation method of Chinese emotional lexicon ontology is used to construct the emotional lexicon of this article. Secondly, considering other emotional polarity characteristics in the dictionary, an emotional tendency analysis model is proposed. In this article, emotional analysis is applied to the evolution analysis of network public opinion, and the change of network public opinion characteristics with time series is obtained. The simulation results show that the emotional dictionary constructed in this article and the proposed model of emotional orientation analysis can effectively analyze the emotional characteristics of network public opinion participants and apply emotional analysis to the evolution analysis of network public opinion, which can get the change of emotional characteristics of public opinion participants with time series.
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Feng, Jinhui, Shaohua Cai, Kuntao Li, Yifan Chen, Qianhua Cai, and Hongya Zhao. "Fusing Syntax and Semantics-Based Graph Convolutional Network for Aspect-Based Sentiment Analysis." International Journal of Data Warehousing and Mining 19, no. 1 (2023): 1–15. http://dx.doi.org/10.4018/ijdwm.319803.
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Aspect-based sentiment analysis (ABSA) aims to classify the sentiment polarity of a given aspect in a sentence or document, which is a fine-grained task of natural language processing. Recent ABSA methods mainly focus on exploiting the syntactic information, the semantic information and both. Research on cognition theory reveals that the syntax an*/874d the semantics have effects on each other. In this work, a graph convolutional network-based model that fuses the syntactic information and semantic information in line with the cognitive practice is proposed. To start with, the GCN is taken to extract syntactic information on the syntax dependency tree. Then, the semantic graph is constructed via a multi-head self-attention mechanism and encoded by GCN. Furthermore, a parameter-sharing GCN is developed to capture the common information between the semantics and the syntax. Experiments conducted on three benchmark datasets (Laptop14, Restaurant14 and Twitter) validate that the proposed model achieves compelling performance comparing with the state-of-the-art models.
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Uddin, Gias, Yann-Gaël Guéhénuc, Foutse Khomh, and Chanchal K. Roy. "An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets." ACM Transactions on Software Engineering and Methodology 31, no. 3 (2022): 1–38. http://dx.doi.org/10.1145/3491211.
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Sentiment analysis in software engineering (SE) has shown promise to analyze and support diverse development activities. Recently, several tools are proposed to detect sentiments in software artifacts. While the tools improve accuracy over off-the-shelf tools, recent research shows that their performance could still be unsatisfactory. A more accurate sentiment detector for SE can help reduce noise in analysis of software scenarios where sentiment analysis is required. Recently, combinations, i.e., hybrids of stand-alone classifiers are found to offer better performance than the stand-alone classifiers for fault detection. However, we are aware of no such approach for sentiment detection for software artifacts. We report the results of an empirical study that we conducted to determine the feasibility of developing an ensemble engine by combining the polarity labels of stand-alone SE-specific sentiment detectors. Our study has two phases. In the first phase, we pick five SE-specific sentiment detection tools from two recently published papers by Lin et al. [ 29 , 30 ], who first reported negative results with stand alone sentiment detectors and then proposed an improved SE-specific sentiment detector, POME [ 29 ]. We report the study results on 17,581 units (sentences/documents) coming from six currently available sentiment benchmarks for software engineering. We find that the existing tools can be complementary to each other in 85-95% of the cases, i.e., one is wrong but another is right. However, a majority voting-based ensemble of those tools fails to improve the accuracy of sentiment detection. We develop Sentisead, a supervised tool by combining the polarity labels and bag of words as features. Sentisead improves the performance (F1-score) of the individual tools by 4% (over Senti4SD [ 5 ]) – 100% (over POME [ 29 ]). The initial development of Sentisead occurred before we observed the use of deep learning models for SE-specific sentiment detection. In particular, recent papers show the superiority of advanced language-based pre-trained transformer models (PTM) over rule-based and shallow learning models. Consequently, in a second phase, we compare and improve Sentisead infrastructure using the PTMs. We find that a Sentisead infrastructure with RoBERTa as the ensemble of the five stand-alone rule-based and shallow learning SE-specific tools from Lin et al. [ 29 , 30 ] offers the best F1-score of 0.805 across the six datasets, while a stand-alone RoBERTa shows an F1-score of 0.801.
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Lim, Vuanghao, Hui Wen Chong, Nozlena Abdul Samad, et al. "Vibrational Spectroscopy-Based Chemometrics Analysis of Clinacanthus nutans Extracts after Postharvest Processing and Extract Effects on Cardiac C-Kit Cells." Evidence-Based Complementary and Alternative Medicine 2022 (February 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/1967593.
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Chemical constituents in plants can be greatly affected by postharvest processing, and it is important to identify the factors that lead to significant changes in chemistry and bioactivity. In this study, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy was used to analyze extracts of Clinacanthus nutan (C. nutans) leaves generated using different parameters (solvent polarities, solid-liquid ratios, ultrasonic durations, and cycles of extraction). In addition, the effects of these extracts on the viability of cardiac c-kit cells (CCs) were tested. The IR spectra were processed using SIMCA-P software. PCA results of all tested parameter sets were within acceptable values. Solvent polarity was identified as the most influential factor to observe the differences in chemical profile and activities of C. nutans extracts. Ideal extraction conditions were identified, for two sample groups (G1 and G2), as they showed optimal total phenolic content (TPC) yield of 44.66 ± 0.83 mg GAE/g dw and 45.99 ± 0.29 mg GAE/g dw and CC viability of 171.81 ± 4.06% and 147.53 ± 6.80%, respectively. Validation tools such as CV-ANOVA ( p < 0.05 ) and permutation (R2 and Q2 plots were well intercepted to each other) have further affirmed the significance and reliability of the partial least square (PLS) model of solvent polarity employed in extraction. Hence, these approaches help optimize postharvest processes that encourage positive TPC and CCs results in C. nutans extracts.
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Nkongolo Wa Nkongolo, Mike. "News Classification and Categorization with Smart Function Sentiment Analysis." International Journal of Intelligent Systems 2023 (November 13, 2023): 1–24. http://dx.doi.org/10.1155/2023/1784394.
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Search engines are tools used to find information on the Internet. Since the web has a plethora of websites, the engine queries the majority of active sites and builds a database organized according to keywords utilized in the search. Because of this, when a user types a few descriptive words on the home page of the search engine, the search function lists websites corresponding to these keywords. However, there are some problems with this search approach. For instance, if a user wants information about the word Jaguar, most search results are animals and cars. This is a polysemic problem that forces search engines to always provide the most popular but not the most relevant results. This article presents a study of using sentiment technology to help news classification and categorization and improve the classification accuracy. We have introduced a smart search function embedded into a search engine to tackle polysemic issues and record relevant results to determine their sentimentality. Therefore, this study presents a topic that involves several aspects of natural language processing (NLP) and sentiment analysis for news categorization and classification. A web crawler was used to collect British Broadcasting Corporation (BBC) news across the Internet, carried out preprocessing of text by using NLP, and applied sentiment analysis methods to determine the polarity of the processed text data. The sentimentality represents negative, positive, or neutral polarities assigned by the sentiment analysis algorithms. The research utilized the BBC news site to collect different information using a web crawler and a database to explore the sentimentality of BBC news. The natural language toolkit (NLTK) and BM25 indexed and preprocessed patterns in the database. The experimental results depict the proposed search function surpassing normal search with an accuracy rate of 85%. Moreover, the results show a negative polarity of BBC news using the Sentistrength algorithm. Furthermore, the Valence Aware Dictionary and sEntiment Reasoner (VADER) was the best-performing sentiment analysis model for news classification. This model obtained an accuracy of 85% using data collected with the proposed smart function.
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Pazin Fadzil, Aini Hayati, Siti Mistima Maat, and Muhammad Sofwan Mahmud. "A Rasch model analysis of the TPACK instrument in the creative teaching of primary mathematics teachers." Cypriot Journal of Educational Sciences 17, no. 11 (2022): 4259–74. http://dx.doi.org/10.18844/cjes.v17i11.7792.
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Utilising the Technological Pedagogical Content Knowledge (TPACK) instrument, mathematics teachers were evaluated on their level of TPACK in their creative teaching practices. Using Rasch model analysis, this study aimed to assess the validity and reliability of the TPACK instrument. A 30-item survey with a 5-point Likert scale was given at random to 77 primary school teachers. In order to analyse the data and evaluate the instrument using the Rasch model analysis test, including the item and person separation and reliability index, misfit items, item polarity and unidimensionality, Winsteps 5.2.2.0 software was used. The findings demonstrated a significant Cronbach's Alpha (KR20). In conclusion, this TPACK instrument has high validity and reliability for assessing knowledge in the creative teaching of mathematics teachers. Keywords: Creative teaching, mathematics, Rasch model analysis, teachers.
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Zhang, Xianhang, Hanchen Wang, Jianke Yu, Chen Chen, Xiaoyang Wang, and Wenjie Zhang. "Polarity-based graph neural network for sign prediction in signed bipartite graphs." World Wide Web 25, no. 2 (2022): 471–87. http://dx.doi.org/10.1007/s11280-022-01015-4.
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AbstractAs a fundamental data structure, graphs are ubiquitous in various applications. Among all types of graphs, signed bipartite graphs contain complex structures with positive and negative links as well as bipartite settings, on which conventional graph analysis algorithms are no longer applicable. Previous works mainly focus on unipartite signed graphs or unsigned bipartite graphs separately. Several models are proposed for applications on the signed bipartite graphs by utilizing the heuristic structural information. However, these methods have limited capability to fully capture the information hidden in such graphs. In this paper, we propose the first graph neural network on signed bipartite graphs, namely Polarity-based Graph Convolutional Network (PbGCN), for sign prediction task with the help of balance theory. We introduce the novel polarity attribute to signed bipartite graphs, based on which we construct one-mode projection graphs to allow the GNNs to aggregate information between the same type nodes. Extensive experiments on five datasets demonstrate the effectiveness of our proposed techniques.
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Xing, Yongping, Chuangbai Xiao, Yifei Wu, and Ziming Ding. "A Convolutional Neural Network for Aspect-Level Sentiment Classification." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 14 (2019): 1959046. http://dx.doi.org/10.1142/s0218001419590468.
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Sentiment analysis, including aspect-level sentiment classification, is an important basic natural language processing (NLP) task. Aspect-level sentiment can provide complete and in-depth results. Words with different contexts variably influence the aspect-level sentiment polarity of sentences, and polarity varies based on different aspects of a sentence. Recurrent neural networks (RNNs) are regarded as effective models for handling NLP and have performed well in aspect-level sentiment classification. Extensive literature exists on sentiment classification that utilizes convolutional neural networks (CNNs); however, no literature on aspect-level sentiment classification that uses CNNs is available. In the present study, we develop a CNN model for handling aspect-level sentiment classification. In our model, attention-based input layers are incorporated into CNN to introduce aspect information. In our experiment, in which a benchmark dataset from Twitter is compared with other models, incorporating aspect information into CNN improves aspect-level sentiment classification performance without using syntactic parser or other language features.
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Mi, Chuanmin, Xiaoyan Ruan, and Lin Xiao. "Microblog Sentiment Analysis Using User Similarity and Interaction-Based Social Relations." International Journal of Web Services Research 17, no. 3 (2020): 39–55. http://dx.doi.org/10.4018/ijwsr.2020070103.
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With the rapid development of information technology, microblog sentiment analysis (MSA) has become a popular research topic extensively examined in the literature. Microblogging messages are usually short, unstructured, contain less information, creating a significant challenge for the application of traditional content-based methods. In this study, the authors propose a novel method, MSA-USSR, in which user similarity information and interaction-based social relations information are combined to build sentiment relationships between microblogging data. They make use of these microblog–microblog sentiment relations to train the sentiment polarity classification classifier. Two Sina-Weibo datasets were utilized to verify the proposed model. The experimental results show that the proposed method has a better sentiment classification accuracy and F1-score than the content-based support vector machine (SVM) method and the state-of-the-art supervised model known as SANT.
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Garcia, Manuel B. "Sentiment Analysis of Tweets on Coronavirus Disease 2019 (COVID-19) Pandemic from Metro Manila, Philippines." Cybernetics and Information Technologies 20, no. 4 (2020): 141–55. http://dx.doi.org/10.2478/cait-2020-0052.
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AbstractFrom the outbreak of a novel COronaVIrus Disease (COVID-19) in Wuhan to the first COVID-19 case in the Philippines, Filipinos have been enthusiastically engaging on Twitter to convey their sentiments. As such, this paper aims to identify the public opinion of Filipino twitter users concerning COVID-19 in three different timelines. Toward this goal, a total of 65,396 tweets related to COVID-19 were sent to data analysis using R Statistical Software. Results show that “mask”, “health”, “lockdown”, “outbreak”, “test”, “kit”, “university”, “alcohol”, and “suspension” were some of the most frequently occurring words in the tweets. The study further investigates Filipinos’ emotions regarding COVID-19 by calculating text polarity of the dataset. To date, this is the first paper to perform sentiment analysis on tweets pertaining to COVID-19 not only in the Filipino context but worldwide as well.
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Diaz Jr., Manuel O. "A Domain-Specific Evaluation of the Performance of Selected Web-based Sentiment Analysis Platforms." International Journal of Software Engineering and Computer Systems 9, no. 1 (2023): 1–9. http://dx.doi.org/10.15282/ijsecs.9.1.2023.1.0105.
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There is now an increasing number of sentiment analysis software-as-a-service (SA-SaaS) offerings in the market. Approaches to sentiment analysis and their implementation as SA-SaaS vary, and there really is no sure way of knowing what SA-SaaS uses which approach. For potential users, SA-SaaS products are black boxes. Black boxes, however, can be evaluated using a set of standard input and a comparison of the output. Using a test data set drawn from human annotated samples in existing studies covering sentiment polarity of news headlines, this study compares the performance of selected popular and free (or at least free-to-try) SA-SaaS in terms of the accuracy, precision, recall and specificity of the sentiment classification using the black box testing methodology. SentiStrength, developed at the University of Wolverhampton in the UK, emerged as consistent performer across all metrics.
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Castro, Amanda, Marieli Mezari Vitali, Andréa Barbará S. Bousfield, and Brigido Vizeu Camargo. "Social representations of the internet for the elderly." Journal of Human Growth and Development 30, no. 2 (2020): 227–40. http://dx.doi.org/10.7322/jhgd.v30.10369.
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Introduction: Increase in Internet access by elderly people is a consequence of population ageing, even though a long way still lies ahead for their digital inclusion.
 Objective: To describe the social representations of Internet among the elderly and to compare objectification and anchoring processes of elderly people with different levels in Internet usage.
 Methods: Qualitative and quantitative study, with descriptive and comparative design, involving forty participants. Data collection occurred through different tools as follows: 1) associative network, analyzed by EVOC2000 and calculation of polarity 2) semi-structured interview, analyzed by Descending Hierarchical Classification with IRaMuTeQ and content analysis by Atlas TI and 3) characterization questionnaire and evaluation scale for digital inclusion level, with descriptive statistical analysis by SPSS software.
 Results: Associative network analyzed 78 words and their polarity was slightly positive. Descending Hierarchical Classification analyzed 89.51% of the corpus, divided into three segments: Internet danger, difficulties in usage v. Internet options and practices; content analysis divided 505 occurrences into three categories: image, attitude and information.
 Conclusion: Representations of elderly people with the highest use of Internet were undertaken by accessing hardware, apps and sites, anchored on an idea of the Internet as a means of retrieving information, leisure and interaction. Representations of the elderly with the lowest level of experience were undertaken by computer, based on a sociological perspective and marked by unfavorable attitudes.
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Bhardwaj, Shubham. "AN INDEPTH ANALYSIS OF CATEGORIZED MINING ALGORITHMS FOR OPINION MINING." International Journal of Research in Science and Technology 10, no. 01 (2022): 53–57. http://dx.doi.org/10.37648/ijrst.v10i01.011.
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Today's information and ideas can't be shared without social media. A person's day-to-day life is significantly affected by their emotional impact. An ecosystem that generates millions of bytes of data daily makes sentiment analysis essential for interpreting these enormous amounts of data. Sentiment analysis, a type of text mining, finds and extracts personal information from various sources, allowing businesses to monitor social sentiment about their brand, product, or service. Simply put, sentiment analysis enables one to ascertain the author's perspective on a topic. Writing is categorized as either positive, neutral, or negative by the software for sentiment analysis. With the help of deep learning algorithms and natural language processing functions, written or spoken sentiments about a topic can be better understood This work uses various machine learning algorithms to conduct sentiment analysis on "tweets." The predominant sentiment will determine the label given to a tweet if the tweet only contains positive, negative, or neutral elements. The study will attempt to classify the tweet's polarity as positive, negative, or neutral.
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Md Saad, Nor Hasliza, Alya Syahirah Zainul Abidi, Zulnaidi Yaacod, Muhamad Mu’az Mohd Ali, and Zhu Kun. "Twitter Sentiment Analysis on Meta: A Lexicon-Based Analysis using Rapidminer." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6 (2023): 259–70. http://dx.doi.org/10.17762/ijritcc.v11i6.7561.
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Meta is a new parent company of Facebook, Instagram, WhatsApp, and more as the tech giant tried to move on from its scandal-plagued social network to its metaverse vision. Meta-trended on Twitter as users worldwide tweeted their opinions about the rebrand. The rebranding of Meta has become a popular topic of debate. Social media users, companies, and investors desire to be a part of the next big thing. In this study, lexicon-based polarity detection automatically classifies data as positive, negative, and neutral. This study will investigate the main sentiments towards Meta and the topic social media users discussed Meta. In this study, 2997 tweets from the 9th to the 10th of November 2021 were scraped from Twitter using RapidMiner. The findings showed that 36% had a positive sentiment, 29% had a negative sentiment, and 35% were neutral about the Mete rebranding announcement. In addition, the topics often tweeted include NFT and stock, reaction, Metaverse, partnership, AR and VR, and policy. This study can help the Meta understand the people's multi-faceted views towards the Meta rebranding.
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Kadem, Burak Yahya. "Studying the properties of ternary PEDOT:PSS thin films for solar cell application." Nanomaterials and Energy 14, no. 2 (2025): 1–10. https://doi.org/10.1680/jnaen.24.00014.
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Poly(3-hexylthiophene):phenyl-C61-butyric acid methyl ester–based organic solar cell is used to investigate the effects of treated poly(3,4-ethylenedioxythiophene):poly(styrenesulfonic-acid (PEDOT:PSS) as a hole transport layer. This layer is treated with different alcohol solutions based on the polarity and mixture. The variation in the polarity of alcohol and their effects on the organic solar cell parameters and behavior were studied and evaluated using different techniques such as atomic force microscopy with the aid of using WSxM software involving the application of two-dimensional fast Fourier transforms analysis. Contact angle measurement is also used to measure the hydrophilicity and hydrophobicity of the PEDOT:PSS solution on the top of substrates. An interdigitated electrode is employed to measure the electrical conductivity of the PEDOT:PSS layers; such results are significant in the solar cell application. The latter is characterized using I–V characteristics and IPCE measurement and the results revealed a good enhancement in the solar cell performance with good stability. Inverted structure is also demonstrated, and the results suggest that the treated PEDOT:PSS layer is promising in the field of inverted OSCs.
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Papapicco, Concetta. "SentiSfaction: New cultural way to measure tourist COVID-19 mobility in Italy." Mediterranean Journal of Social & Behavioral Research 7, no. 1 (2023): 29–41. http://dx.doi.org/10.30935/mjosbr/12790.
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From a psycho-linguistic and marketing perspective, the research fits into the evaluation of in the context of tourism and, in particular, tourism mobility, targeting one of the leading Italian rail transport companies, namely Trenitalia. This study, conducted on tweets, aims to examine how talks about the transport service offered by Trenitalia. A total of 674 tweets for the tourist season 2019 and 100 tweets for the tourist season 2020 were collected following the pre-COVID-19 and COVID-19 period. The methodology is the application of sentiment analysis (SA) that produces quantitative and qualitative results. For the quantitative part, the sentiment was calculated first automatically via the Sentistrength software, then an extraction of the frequencies and calculation of the dependence (Chi-square statistic and t-test) between year and polarity was conducted with R, statistical software. The results show that SA is a good methodology of analysis of the online reputation and customer satisfaction of a company that deals with tourism, also in the difference between pre-COVID-19 and COVID-19 period.
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Jung, Soon-Gyo, Joni Salminen, and Bernard J. Jansen. "Engineers, Aware! Commercial Tools Disagree on Social Media Sentiment: Analyzing the Sentiment Bias of Four Major Tools." Proceedings of the ACM on Human-Computer Interaction 6, EICS (2022): 1–20. http://dx.doi.org/10.1145/3532203.
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Large commercial sentiment analysis tools are often deployed in software engineering due to their ease of use. However, it is not known how accurate these tools are, and whether the sentiment ratings given by one tool agree with those given by another tool. We use two datasets - (1) NEWS consisting of 5,880 news stories and 60K comments from four social media platforms: Twitter, Instagram, YouTube, and Facebook; and (2) IMDB consisting of 7,500 positive and 7,500 negative movie reviews - to investigate the agreement and bias of four widely used sentiment analysis (SA) tools: Microsoft Azure (MS), IBM Watson, Google Cloud, and Amazon Web Services (AWS). We find that the four tools assign the same sentiment on less than half (48.1%) of the analyzed content. We also find that AWS exhibits neutrality bias in both datasets, Google exhibits bi-polarity bias in the NEWS dataset but neutrality bias in the IMDB dataset, and IBM and MS exhibit no clear bias in the NEWS dataset but have bi-polarity bias in the IMDB dataset. Overall, IBM has the highest accuracy relative to the known ground truth in the IMDB dataset. Findings indicate that psycholinguistic features - especially affect, tone, and use of adjectives - explain why the tools disagree. Engineers are urged caution when implementing SA tools for applications, as the tool selection affects the obtained sentiment labels.
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Samonte, Benjie R. "Bilingual Feedback Management System for Frontline Services with Sentiment Analysis using Naïve-Bayes Algorithm." Innovatus 2, no. 1 (2019): 83–88. https://doi.org/10.5281/zenodo.5209576.
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The purpose of this research is to develop a feedback management system that uses a modern approach of technologies to aid the existing feedback management system used in the university. The study employed Sentiment Analysis using Naïve-Bayes Algorithm which was used in determining the polarity of the customers' feedbacks or suggestions. In order to come up with an effective and reliable system, the researcher adopted the incremental software development model as software methodology, wherein it delivers a series of releases, called increments. It progressively provides more functionality for the customer as each increment is delivered. One hundred eight (108) customers including seven office heads and one quality management staff were chosen as the respondents of the study. Based on the findings, the developed feedback management system (mobile and web applications) was effective in terms of its overall ease of use, portability and functionality for it received a respectable rating from all respondents. It also showed that the system has passed the overall criteria of its technical quality as well as it eliminates the identified common problems encountered using the existing system. Likewise, the system provides performance reports of each office to determine which among the offices are performing well based on feedbacks. Significantly, this innovation will be an effective feedback mechanism tool in the University to address the concerns of the customers and other stakeholders and provide possible merits and rewards to performing offices.
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Rodríguez Fernández-Peña, Alfonso Carlos. "AI is great, isn’t it? Tone direction and illocutionary force delivery of tag ques-tions in Amazon’s AI NTTS Polly." Journal of Experimental Phonetics 32 (November 28, 2023): 227–42. http://dx.doi.org/10.1344/efe-2023-32-227-242.
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This work provides a descriptive analysis of the tone direction and its inherent illocutionary force in question tags delivered by Amazon’s neural text-to-speech system Polly. We included three types of tag questions (reverse-polarity tags — both positive and negative —, copy tags and command tags) for which 10 sentences were used as input in each case. The data included 600 utterances produced by British and American English voices currently available on Amazon’s NTTS. The audio files were examined with the speech analysis software Praat to identify the tone pattern for each utterance and confirm the intended illocutionary force. The results show that Amazon’s AI speech synthesis technology is not yet fully reliable and produces a high rate of utterances whose pragmatic load is undesired when using natural spontaneous speech traits as question tags.
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Kamaria, Priyanka, and Neha Kawathekar. "Evaluation of contribution of different molecular fragments on antibacterial activity of Schiff bases of indole-3-aldehyde based on QSAR study." Canadian Journal of Chemistry 91, no. 12 (2013): 1174–78. http://dx.doi.org/10.1139/cjc-2013-0122.
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The paper describes the QSAR analysis of a series of 22 Schiff bases of indole-3-aldehyde employing the Hansch approach. Various physicochemical and steric parameters were calculated using the Chem 3D package of molecular modeling Software Chemoffice 2004. QSAR models were generated employing the sequential multiple regression method. Models were validated using leave-one-out and bootstrapping methods. Results obtained show that dipole–dipole energy, LUMO, and total energy play an important role, as their positive contribution is seen in the models. Findings of the present study reveal that substituents that cause increase in flexibility, a decrease in polarity, and electron withdrawing in nature are favorable for antibacterial activity of Schiff bases.
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Vijaylakshmi, Sajwan, Awasthi Monisha, Goel Ankur, and Sharma Priyank. "Sentiment analysis of Twitter data regarding the agnipath scheme of the defense forces." Sentiment analysis of Twitter data regarding the agnipath scheme of the defense forces 30, no. 3 (2023): 1643–50. https://doi.org/10.11591/ijeecs.v30.i3.pp1643-1650.
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Due to the popularity of social media today, people frequently share such criticism on Facebook, Twitter, Instagram, and other platforms. Therefore needs to know how your input from users of social media is generated in order to ascertain the public reaction to the policy that has been enacted. However, because of the comments, it is challenging to tell how many people have responded positive or negative. The objective of sentiment analysis of tweets is to provide insight into people’s attitudes and perceptions regarding an event. This study illustrates the role of Twitter in the announcement of a new army vacancy through the “agnipath scheme” dubbed “agniveer”. The result of this study can be used by the defense forces and government for decision making or policies related to the agnipath scheme. The study studied 4,000 English-language Twitter posts from July 3, 2022 to July 9, 2022. Manual text analysis revealed seven basic groups of tweet sentiments. The tweets’ positive, negative, and neutral emotions were shown using orange data mining software, a powerful machine learning, data mining, and data visualization toolset. Result shows that agnipath scheme is mostly accepted by the people.
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Ali, Manal Mostafa. "Arabic sentiment analysis about online learning to mitigate covid-19." Journal of Intelligent Systems 30, no. 1 (2021): 524–40. http://dx.doi.org/10.1515/jisys-2020-0115.
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Abstract The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. Second, the National Research Council Canada (NRC) Word-Emotion Lexicon was examined to calculate the presence of the eight emotions at their emotional weight. Third, Information Gain (IG) is used as a filtering technique. Fourth, the latent reasons behind the negative sentiments were recognized and analyzed. Finally, different classification algorithms including Naïve Bayes (NB), Multinomial Naïve Bayes (MNB), K Nearest Neighbor (KNN), Logistic Regression (LR), and Support Vector Machine (SVM) were examined. The experiments reveal that the proposed model performs well in analyzing the perception of people about coronavirus with a maximum accuracy of about 89.6% using SVM classifier. From a practical perspective, the method could be generalized to other topical domains, such as public health monitoring and crisis management. It would help public health officials identify the progression and peaks of concerns for a disease in space and time, which enables the implementation of appropriate preventive actions to mitigate these diseases.
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Ahmadin, Hashem, Karim Zare, Majid Monajjemi, and Ali Shamel. "MÉTODOS COMPUTACIONALES PARA LA DELAMINACIÓN DE GRAFITO UTILIZANDO SURFACTANTES ANIONICOS PARA PRODUCIR GRAFENO." Revista de Investigaciones Universidad del Quindío 31, no. 1 (2019): 41–53. http://dx.doi.org/10.33975/riuq.vol31n1.276.
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Today, using thermal and chemical reduction and solubility, graphene oxide is produced in large scale. Since there are various methods for producing graphene, each of which allocates properties to the produced graphene, the purpose of this research is to investigate graphite delamination using anionic surfactants and produce graphene by means of computational methods. The research method was applied in order to perform molecular dynamics analysis, first, (the minimum) force that each atom imposes to other atoms was calculated. This is the total gradient of the system’s energy according to the coordinates of the related atom. A Bayesian method was used for dynamic modeling, which, on average, uses dynamic parameters instead of their estimates. The Gaussian Process Dynamic Model (GPDM) was completely defined by a set of low-level data representations and was observed by both dynamics and modeling of GP regression (Gaussian process regression). Then, using the Gaussian software, along with empirical results or just using this software, the molecular state and reactions and their mechanisms were simulated. The results indicated that the presence of benzene, ether and carbo xyl groups in the optimal structure facilitates the entry of surfactants into the sheets and that the agent to start the separation of the graphene sheets adhered to each other by comparing the results of this study between the two surfactants, it was found that the gap to change by separation layers between the graphene plates is different for two surfactants. Besides, the difference in the polarity of the surfactants resulted in the final polarization of the surfactant and graphene system. Therefore, the difference in the polarity causes the difference in the solubility.
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K. R. Srinath, Et al. "Context-Preserving Sentiment Classification Using Bi-TCN And BI-GRU with Multi-Head Self-Attention." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2836–46. http://dx.doi.org/10.17762/ijritcc.v11i9.9373.
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In natural language processing, sentiment classification is the recently used topic. Specifically, the objective of the sentiment analysis is to categorise the polarity expressed on the sentence's target. However, there are some researches for classifying the polarity of the target which outperforms well in their way. Yet, there are some limitations, such as apparent and in-apparent issues, gradient problems, etc., to overcome these issues the context-preserving sentiment classification using BI-TCN (Bidirectional Temporal Convolutional network) and BI-GRU (Bidirectional Gated Recurrent Unit) with Multi-head self-attention is proposed to extracts both the local dependent and global dependent information from the sentence, then it will incrementally extract the supervision information of the target to train the model. Formerly, the model is tested and trained using four datasets and the performance is compared with four existing methods, its accuracy is evaluated using the F1-score, precision, recall, specificity, and MCC (Matthews Correlation Coefficient). Consequently, the proposed approach provides the best accuracy level of 98%..