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Sentiment analysis tools are frequently employed to analyze large amounts of natural language data gathered from social networks and generate valuable insights on public opinion. Research has discovered that these tools tend to be biased against some demographic groups, based on social attributes such as gender, age, and ethnicity. Sentiment classification works dealt with this issue by means of data balancing and algorithmic approaches. However, one crucial limitation of existing methods is the inability to tackle social bias while maintaining satisfactory model performance. In this paper, we aim to fill this gap by proposing a sentiment classification method that entails ethnicity-aware algorithmic design. Specifically, our method involves balanced training and a custom ethnicity-aware loss function that leverages ethnicity group information to foster a fair model optimization process. The proposed loss incentivizes the model to iteratively improve accuracy for currently underperforming demographic or social groups, therefore simultaneously decreasing social bias and boosting overall performance. Our extensive qualitative and quantitative experimental evaluation involving a large corpus of user reviews demonstrated the effectiveness of the proposed method, also when compared to popular baselines for sentiment classification. [ABSTRACT FROM AUTHOR]

Many text mining methods use statistical information as a text- and language-independent approach for sentiment analysis. However, text mining methods based on stochastic patterns and rules require many samples for training. On the other hand, deterministic and non-probabilistic methods are easier and faster to solve than other methods, but they are inefficient when dealing with Natural Language Processing (NLP) data. This research presents a novel hybrid solution based on two mathematical approaches combined with a heuristic approach to solve unbalanced pseudo-linear algebraic equation systems that can be used as a sentiment word scoring system. In its first step, the proposed solution uses two mathematical approaches to find two initial populations for a heuristic method. The heuristic solution solves a pseudo-linear NLP scoring scheme in a polarity detection method and determines the final scores. The proposed solution was validated using three scenarios on the SemEval-2013 competition, the ESWC dataset, and the Taboada dataset. The simulation results revealed that the proposed solution is comparable to the best state-of-the-art methods in polarity detection. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is a very popular research topic in the text mining field. SA is the process of textual mining in which the meaning of a text is detected and extracted. One of the key aspects of SA is to analyze the body of a text to determine its polarity to understand the opinions it expresses. Substantial amounts of data are produced by online resources such as social media sites, blogs, news sites, etc. Due to this reason, it is impossible to process all of this data without automated systems, which has contributed to the rise in popularity of SA in recent years. SA is considered to be extremely essential, mostly due to its ability to analyze mass opinions. SA, and Natural Language Processing (NLP) in particular, has become an overwhelmingly popular topic as social media usage has increased. The data collected from social media has sourced numerous different SA studies due to being versatile and accessible to the masses. This survey presents a comprehensive study categorizing past and present studies by their employed methodologies and levels of sentiment. In this survey, Turkish SA studies were categorized under three sections. These are Dictionary-based, Machine Learning-based, and Hybrid-based. Researchers can discover, compare, and analyze properties of different Turkish SA studies reviewed in this survey, as well as obtain information on the public dataset and the dictionaries used in the studies. The main purpose of this study is to combine Turkish SA approaches and methods while briefly explaining its concepts. This survey uniquely categorizes a large number of related articles and visualizes their properties. To the best of our knowledge, there is no such comprehensive and up-to-date survey that strictly covers Turkish SA which mainly concerns analysis of sentiment levels. Furthermore, this survey contributes to the literature due to its unique property of being the first of its kind. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) is a natural language processing technique that seeks to recognize and extract the sentiment connected to various qualities or aspects of a specific good, service, or entity. It entails dissecting a text into its component pieces, determining the elements or aspects being examined, and then examining the attitude stated about each feature or aspect. The main objective of this research is to present a comprehensive understanding of aspect-based sentiment analysis (ABSA), such as its potential, ongoing trends and advancements, structure, practical applications, real-world implementation, and open issues. The current sentiment analysis aims to enhance granularity at the aspect level with two main objectives, including extracting aspects and polarity sentiment classification. Three main methods are designed for aspect extractions: pattern-based, machine learning and deep learning. These methods can capture both syntactic and semantic features of text without relying heavily on high-level feature engineering, which was a requirement in earlier approaches. Despite bringing traditional surveys, a comprehensive survey of the procedure for carrying out this task and the applications of ABSA are also included in this article. To fully comprehend each strategy's benefits and drawbacks, it is evaluated, compared, and investigated. To determine future directions, the ABSA's difficulties are finally reviewed. [ABSTRACT FROM AUTHOR]

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Many text mining methods use statistical information as a text- and language-independent approach for sentiment analysis. However, text mining methods based on stochastic patterns and rules require many samples for training. On the other hand, deterministic and non-probabilistic methods are easier and faster to solve than other methods, but they are inefficient when dealing with Natural Language Processing (NLP) data. This research presents a novel hybrid solution based on two mathematical approaches combined with a heuristic approach to solve unbalanced pseudo-linear algebraic equation systems that can be used as a sentiment word scoring system. In its first step, the proposed solution uses two mathematical approaches to find two initial populations for a heuristic method. The heuristic solution solves a pseudo-linear NLP scoring scheme in a polarity detection method and determines the final scores. The proposed solution was validated using three scenarios on the SemEval-2013 competition, the ESWC dataset, and the Taboada dataset. The simulation results revealed that the proposed solution is comparable to the best state-of-the-art methods in polarity detection.

Defect detection is a critical element in the PCB manufacturing process. Different from surface mount PCB, the components on the plug-in PCB are usually installed manually, resulting in significant errors. We make contributions in the following two aspects: (1) a framework and measurement method of a light source and make a cheap and efficient lighting system; (2) a fusion algorithm based on machine learning and morphology for polarity detection of plug-in capacitors. The capacitor is detected using SVM and fused with the polar coordinate expansion method. The AOI system and the proposed fusion algorithm have been applied to the production line, with an accuracy of 99.73 % and a missed detection rate 0.12 % . [ABSTRACT FROM AUTHOR]

The heavy involvement of the Arabic internet users resulted in spreading data written in the Arabic language and creating a vast research area regarding natural language processing (NLP). Sentiment analysis is a growing field of research that is of great importance to everyone considering the high added potential for decision-making and predicting upcoming actions using the texts produced in social networks. Arabic used in microblogging websites, especially Twitter, is highly informal. It is not compliant with neither standards nor spelling regulations making it quite challenging for automatic machine-learning techniques. In this paper's scope, we propose a new approach based on AutoML methods to improve the efficiency of the sentiment classification process for dialectal Arabic. This approach was validated through benchmarks testing on three different datasets that represent three vernacular forms of Arabic. The obtained results show that the presented framework has significantly increased accuracy than similar works in the literature. [ABSTRACT FROM AUTHOR]

In recent years, deep learning-based sentiment analysis has received attention mainly because of the rise of social media and e-commerce. In this paper, we showcase the fact that the polarity detection and subjectivity detection subtasks of sentiment analysis are inter-related. To this end, we propose a knowledge-sharing-based multitask learning framework. To ensure high-quality knowledge sharing between the tasks, we use the Neural Tensor Network, which consists of a bilinear tensor layer that links the two entity vectors. We show that BERT-based embedding with our MTL framework outperforms the baselines and achieves a new state-of-the-art status in multitask learning. Our framework shows that the information across datasets for related tasks can be helpful for understanding task-specific features. [ABSTRACT FROM AUTHOR]

In this paper, we propose an extended permanent magnet synchronous motor model that incorporates a quadratic flux-current function to determine the polarity-dependent saliency and develop a sensorless initial position detection method that is capable of polarity detection. The quadratic model extension is based on the second-order Taylor polynomial of the flux-current function. We present the model equations in both the stator- and the rotor-oriented reference frames as well as the Park and inverse Park transforms of the quadratic terms and Hessian matrices. The transient behavior and the dependency of the extended model on the rotor position have been validated. The proposed initial position detection method injects non-modulated even square wave voltage signals and relies only on phase-current measurements. We analyzed the effect of the pulse length on the errors of the position estimation and polarity detection. We provide machine parameter-based approximations of the current components dependent on the rotor position and, based on them, present a quantitative pulse-length design method.

Sentiment Analysis and Opinion Mining is a computational text processing technique used for extraction of emotion from the given text, for e.g. joy, sad, disgust, fear, anger which can be polarized as positive, negative and neutral. The advancement in technology and data augmentation has been an easy access for data collection for the research work. In this research paper, different machine learning algorithms such as Extreme Gradient Boost classifier, Extra tree classifier, K-Nearest Neighbour classifier and Long Short Term Memory deep learning model was computed to classify the divergent datasets such as Twitter Bengaluru traffic data, movie review, fake news classification and financial sentiments. Performance of these classifier was tabulated with respect to independent datasets in terms of accuracy, f1-score, precision, sensitivity, mean squared error and, log loss and graphically represented with the help of PyCharm. The contribution in this paper is a methodology that automates the sentiment analysis of the divergent dataset with context of apprehension of tweets. [ABSTRACT FROM AUTHOR]

Customer reviews on the Internet reflect users' sentiments about the product, service, and social events. As sentiments can be divided into positive, negative, and neutral forms, sentiment analysis processes identify the polarity of information in the source materials toward an entity. Most studies have focused on document-level sentiment classification. In this study, we apply an unsupervised machine learning approach to discover sentiment polarity not only at the document level but also at the word level. The proposed topic document sentence (TDS) model is based on joint sentiment topic (JST) and latent Dirichlet allocation (LDA) topic modeling techniques. The IMDB dataset, comprising user reviews, was used for data analysis. First, we applied the LDA model to discover topics from the reviews; then, the TDS model was implemented to identify the polarity of the sentiment from topic to document, and from document to word levels. The LDAvis tool was used for data visualization. The experimental results show that the analysis not only obtained good topic partitioning results, but also achieved high sentiment analysis accuracy in document- and word-level sentiment classifications. [ABSTRACT FROM AUTHOR]

Cellular micro-environment analysis via fluorescence probe has become a powerful method to explore the early-stage cancer diagnosis and pathophysiological process of relevant diseases. The polarity change of intracellular lipid droplets (LDs) is closely linked with disorders or diseases, which result in various physiological and pathological processes. However, the efficient design strategy for lipid droplet polarity probes with high sensitivity is lacking. To overcome this difficulty, two kinds of LDs-targeting and polarity-sensitive fluorescent probes containing carbazole and siloxane groups were rationally designed and synthesized. With the carbazole-based rotor and bridge-like siloxanes, two probes (P1 and P2) behave high sensitivity to polarity changes and show different fluorescent intensity in normal and cancer cells. Notably, polysiloxanes groups promoted the response sensitivity of the probes dramatically for the polymeric microenvironment. In addition, due to the polarity changes of LDs in cancer cells, the distinct fluorescent intensities in different channels of laser scanning confocal microscope were observed between NHA cell and U87 cells. This work could offer an opportunity to monitor the dynamic behaviors of LDs and further provide a powerful tool to be potentially applied in the early-stage diagnosis of cancer. Two kinds of carbazole-based LDs fluorescence probes with remarkable solvatochromic effect to polarity, which can able to distinguish cancerous and normal cells via distinct emission of fluorescence signal. [Display omitted] • The polymeric microenvironment could enhance the polarity response sensitivity. • LDs-targeting and polarity-sensitive probes were designed. • The distinct fluorescent intensities were observed between different cells. [ABSTRACT FROM AUTHOR]

Students' feedback is crucial for academic institutions in order to evaluate faculty performance. Handling the qualitative opinions of students efficiently while automatic report generation is a challenging task. Indeed, most organizations deal with quantitative feedback effectively, whereas qualitative feedback is either processed manually or ignored altogether. This study proposes a supervised aspect based opinion mining system based on two-layered LSTM model. The first layer predicts the aspects described within the feedback and later specifies the orientation (positive, negative, and neutral) of those predicted aspects. The model was tested on a manually tagged data set constructed from the last five years students' comments from Sukkur IBA University as well as on a standard SemEval-2014 data set. Unlike many other LSTM models proposed for other domains, the proposed model is quite simple in terms of architecture which results in less complexity. The system attains a good accuracy using the domain embedding layer in both tasks: aspect extraction (91%) and sentiment polarity detection (93%). To the best of our knowledge, this study is a first attempt that uses deep learning approach for performing aspect based sentiment analysis on students' feedback for evaluating faculty teaching performance.

In recent years, deep learning-based sentiment analysis has received attention mainly because of the rise of social media and e-commerce. In this paper, we showcase the fact that the polarity detection and subjectivity detection subtasks of sentiment analysis are inter-related. To this end, we propose a knowledge-sharing-based multitask learning framework. To ensure high-quality knowledge sharing between the tasks, we use the Neural Tensor Network, which consists of a bilinear tensor layer that links the two entity vectors. We show that BERT-based embedding with our MTL framework outperforms the baselines and achieves a new state-of-the-art status in multitask learning. Our framework shows that the information across datasets for related tasks can be helpful for understanding task-specific features.

In this paper, a novel extended permanent magnet synchronous motor model is presented that incorporates a quadratic flux-current function to represent the polarity-dependent saliency. The proposed model enables the design of sensorless polarity detection algorithms required by the initial position detection of permanent magnet synchronous motors. The novelty of the model is that it integrates the polarity-dependent saliency into the traditional machine model and introduces a new machine parameter, the polarity-dependent saliency coefficient, to specify the Hessian matrix of the flux-current function. A measurement method is presented for determination of the elements of the Hessian and the polarity-dependent saliency coefficient. The solution of the model is given for high-frequency sinusoidal pulsating voltage injection. Experimental results show that the proposed extended model accurately predicts the amplitudes and phases of the second harmonics of the motor currents, which are the carriers of the polarity-dependent information. This information enables a current measurement-based polarity detection algorithm using the phase difference between the fundamental and second harmonic of the apparent d-axis current. Both the presented measurement data and the proposed model show that injection in the d-direction is optimal for polarity detection.

This paper compares three shaft-sensorless control schemes for wound-field flux-switching machines based on a high frequency (HF) square-wave voltage injection technique. An HF model for the machine, with inclusion of the field winding, is first presented. Because the machine had armature and field windings, HF voltage is injected into and processed in the armature winding as with conventional permanent magnet synchronous machines (PMSMs) or injected into one winding and processed in the induced current signal in the other winding. Both the analytical and experimental results reveal that the scheme with d-axis voltage injection and a q-axis induced current process is generally similar to that used for PMSMs. Polarity identification is required to prevent phase errors in the estimated position. The schemes with separate windings for voltage injection and current processing demonstrate superior performance compared with the scheme without, and both schemes do not require polarity identification. Moreover, voltage injection at the field winding has an additional advantage for high-speed operations.

With the proliferation of social media and mobile technology, huge amount of unstructured data is posted daily online. Consequently, sentiment analysis has gained increasing importance as a tool to understand the opinions of certain groups of people on contemporary political, cultural, social or commercial issues. Unlike western languages, the research on sentiment analysis for dialectical Arabic language is still in its early stages with several challenges to be addressed. The main goal of this study is twofold. First, it compares the performance of core machine learning algorithms for detecting the polarity in imbalanced Arabic tweet datasets using neural word embedding as a feature extractor rather than hand-crafted or traditional features. Second, it examines the impact of using various oversampling techniques to handle the highly-imbalanced nature of the sentiment data. Intensive empirical analysis of nine machine learning methods and six oversampling methods has been conducted and the results have been discussed in terms of a wide range of performance measures. [ABSTRACT FROM AUTHOR]

[Display omitted] • CsPbBr 3 with quantum-limited domain effects have reversible hydrochromic properties. • Different Organic solvents cause polarity-related discoloration of composites. • A detection model was developed for all-organic solvent polarity detection. Hydrochromic materials have received much attention in anticounterfeiting due to their reversible photoemission properties toward water. However, existing hydrochromic materials usually suffer from slow hydrochromic response and limited stability, repeatability, and color tunability. This paper found for the first time that CsPbBr 3 /Cs 4 PbBr 6 composites with quantum-limited domain effects had reversible hydrochromism. CsPbBr 3 /Cs 4 PbBr 6 composites were successfully synthesized in molecular sieve MCM-41 using an improved mechanochemical method. The composites underwent a reversible phase transition from Cs 4 PbBr 6 /CsPbBr 3 to CsPbBr 3 upon treatment with a trace amount of water, the photoluminescence colors underwent a change from blue to green, and reversion occurred due to heating. Mechanistic investigation revealed that quantum dots underwent soft agglomeration due to van der Waals forces in polar solvent environments, affecting the action of quantum domain-limiting effects. Therefore, organic solvents with different polarities can cause different degrees of reversible discoloration of the material. This study provides new ideas for understanding the phase transition and agglomeration of metal halide perovskite quantum dots in polar solvent environments. Additionally, it offered new options for aqueous color-changing materials and organic solvent polar detection materials. [ABSTRACT FROM AUTHOR]

Customer reviews on the Internet reflect users’ sentiments about the product, service, and social events. As sentiments can be divided into positive, negative, and neutral forms, sentiment analysis processes identify the polarity of information in the source materials toward an entity. Most studies have focused on document-level sentiment classification. In this study, we apply an unsupervised machine learning approach to discover sentiment polarity not only at the document level but also at the word level. The proposed topic document sentence (TDS) model is based on joint sentiment topic (JST) and latent Dirichlet allocation (LDA) topic modeling techniques. The IMDB dataset, comprising user reviews, was used for data analysis. First, we applied the LDA model to discover topics from the reviews; then, the TDS model was implemented to identify the polarity of the sentiment from topic to document, and from document to word levels. The LDAvis tool was used for data visualization. The experimental results show that the analysis not only obtained good topic partitioning results, but also achieved high sentiment analysis accuracy in document- and word-level sentiment classifications.

One of the primary tasks of opinion mining is the classification of the polarity of the opinion, that consists in determining whether the opinion is positive or negative with respect to the entity to which it is referring. This work seeks design a general scheme consists in five stages that enable the polarity detection of opinions in an unsupervised manner. The five considered stages are: token identification, lexical disambiguation, get all meanings of each word, classify each token in positive or negative, and evaluate the opinion. PosNeg Opinion detects the polarity of the opinions in Spanish language. The proposed system showed good performance in classifying the polarity of 200 opinions from discussion forums Yahoo.es, yielding an accuracy of 0.965 and a precision of 0.970.

Abstract: The information from social media is emerging as a valuable source in decision-making, unfortunately the tools to turn these data into useful information still need some work. Using Support Vector Machines for polarity detection in short texts are popular among researchers for their good results, but parameter optimization to train classification models is a complex and costly process. This article compares two algorithms for automated parameter optimization in the process of creating classification models for polarity detection: the recently created Grey Wolf Optimizer and the Grid Search, using accuracy and f-score metrics. Spanish Abstract: Los datos provenientes de las redes sociales están emergiendo como una fuente valiosa de información para los procesos de toma de decisiones, desafortunadamente las herramientas para convertir estos datos en información útil todavía tienen mucho camino por recorrer. Utilizar máquinas de soporte vectorial para la detección de polaridad en textos cortos goza de popularidad entre los investigadores debido a sus buenos resultados. Sin embargo, la optimización de los parámetros necesarios para entrenar modelos es un proceso complejo y costoso. Este artículo compara dos algoritmos para la optimización automatizada de parámetros en el proceso de crear modelos de clasificación para la detección de polaridad: Optimizador de lobo gris y las búsqueda en malla, utilizando las métricas de precisión y valor-f.