Business Sentiment Quotient Analysis using Natural Language Processing

World wide web is abundant with various types of information such blogs, social media posts, news articles. With this type of magnitude of online content, there is a need to deeply understand the insights of it in order to make use of the information for practical applications such as event detection, polarity, sentiment analysis and so on. Natural Language Processing (NLP) is the study of such information which is used for text classification, sentiment analysis, clustering of similar text. NLP makes use of linguistic knowledge and build machine learning models to analyse textual information. NLP finds its way in various applications like classification of online review into positive and negative without actually reading the reviews and feedback. For text analysis, there should be a way to quantify the text based on its frequency of occurrence, correlation with neighbouring words, contextual similarity of words, etc. One such way is word embedding. This study applies various word embedding techniques on tweets of popular news channels and clusters the resultant vectors using K-means algorithm. From this study, it is found out that Bidirectional Encoder Representations from Transformers (BERT) has achieved highest accuracy rate when used with K-means clustering.

This paper discusses the potential of machine learning, data science, and natural language processing (NLP) in mitigating the incidence of spoofing and financial risks hinged on cyber threats. Another one is spoofing; it is the act of impersonating legitimate entities to gain unauthorized information, and it is indeed a threat to the public and companies to some extent. The research introduces two primary methodologies to combat spoofing: an email filtering system using a machine learning algorithm and an encryption and decryption system using a Caesar Cipher and Python programming language. It distinguishes between approved domains and unapproved domains by using machine learning and successfully filters out phishing emails from reaching the intended clients. This study also illustrates how to conduct email domain verification using MongoDB Atlas, which a database is containing approved vendors’ domains, to reduce spoofing. Specifically, incorporating NLP helps the system analyze raw data to categorize it and identify patterns potentially leading to a spoofing attempt, enhancing the spoofing detection and prevention of the system. The paper also presents arguments that require awareness and integration of new technologies in the security frameworks. Hence, incorporating machine learning, data science, and NLP presents robust, versatile, and cost-effective solutions to enhance cybersecurity and ultimately protect vital information and organizations’ monetary loss due to cybercrimes. The paper was first completed in 2021 and later I modified the article with latest updates till date 2024.

Learning semantic information from Internet Domain Names using word embeddings

Many researchers in various disciplines have focused on extracting meaningful information from social media platforms in recent years. Identification of behaviors and emotions from user posts is examined under the heading of sentiment analysis (SA) studies using the natural language processing (NLP) techniques. In this study, a novel TCNN-Bi-LSTM model using the two-stage convolutional neural network (TCNN) and bidirectional long short-term memory (Bi-LSTM) architectures was proposed. While TCNN layers enable the extraction of strong local features, the output of these layers feeds the Bi-LSTM model that remembers forward-looking information and capture long-term dependencies. In this study, first, preprocessing steps were applied to the raw dataset. Thus, strong features were extracted from the obtained quality dataset using the FastText word embedding technique that pre-trained with location-based and sub-word information features. The experimental results of the proposed method are promising compared to the baseline deep learning and machine learning models. Also, experimental results show that while the FastText data embedding technique achieves the best performance compared to other word embedding techniques in all deep learning classification models, it has not had the same outstanding success in machine learning models. This study aims to investigate the sentiments of tweets about the COVID-19 vaccines and comments on these tweets among Twitter users by using the power of Twitter data. A new dataset collected from Twitter was constructed to be used in experimental results. This study will facilitate detecting inappropriate, incomplete, and erroneous information about vaccination. The results of this study will enable society to broaden its perspective on the administered vaccines. It can also assist the government and healthcare agencies in planning and implementing the vaccination's promotion on time to achieve the herd immunity provided by the vaccination.

7 Background: Natural Language Processing (NLP) presents a novel method of extracting text-embedded information from the electronic health record (EHR) to improve routine assessment of palliative quality metrics such as timely advance care planning (ACP), palliative care provision (PC), and hospice referral. Methods: We identified cancer patients (ICD-9-CM codes 140-209) who received a gastrostomy tube (ICD-9-CM 43.11, 43.19, 44.32; CPT code 49440) from Jan 1, 2012, to Mar 31, 2016 at an academic medical center. We used NLP to identify palliative indication for gastrostomy tube placement by labeling clinical notes from the EHR containing the key word “venting” near the time of the procedure. Documentation of ACP, PC, and hospice referral was identified by NLP using a validated key term library. The sensitivity and specificity of the NLP method was determined by comparing outcome identification to manual chart abstraction performed by two clinicians. All NLP code was written in the open-source programming language Python. Results: NLP was performed for 75,626 documents. Among 305 cancer patients who underwent gastrostomy, 75 (24.6%) were classified by NLP as having a palliative indication for the procedure compared to 72 patients (23.6%) classified by human coders. Manual chart abstraction took > 2,600 times longer than NLP (28 hrs vs. 38 seconds). NLP identified the correct patients with high precision (0.92) and recall (0.96). ACP was documented during the index admission for 89.3% of patients. PC was documented for 85.7% and hospice referral was documented for 64.3% of these patients with advanced cancer during the index hospitalization. NLP identified ACP, PC and hospice referral with high precision (0.88-1.0) and recall (0.92-1.0) compared to human coders. Median survival was 37 days following gastrostomy tube procedure. Conclusions: NLP can greatly speed the assessment of established palliative quality metrics with an accuracy approaching that of human coders. These methods offer opportunities for facilitate quality improvement in palliative care for patients with advanced cancer.

The identification of a speaker’s intent is the most important task in today’s customer relationship management. Each and every task in CRM is associated with a customer. An organization’s turnover will always be based on the way they satisfy their customers/clients. In order to have a summary of the services reach, the working team has to analyze the responses obtained from the customer. That way of analyzing the outcome obtained from the customer is done with artificial intelligence, a study of training the machines. One such subspace of AI is machine learning. One of the advantages of machine learning is that it is helpful in making decisions and predictions based on the data that it gets trained. The data can be of any kind, relative to the day-to-day research. Such a way of understanding, analyzing and predicting the accurate outcome is again done with natural language processing, which is a field of ML. NLP is the field of computer science that is helpful in training the human language to the machines. As seen above, our research on customer response is related to NLP. The customer’s response is taken as input for the text analysis available among the NLP algorithms. From the analysis made, the mood or intent of the customer is delivered as a result. The programming language Python is used for the entire processing of the text analysis.KeywordsNatural language processing (NLP)TokenizationPart of speech (POS) taggingSentiment analysisPositiveNegativeNeutralSentencePolaritySensitivitySubjectivity

Latest Trends in Twitter from Arab Countries and the World

Advancing radar-based precipitation nowcasting : an open benchmark and the potential of deep learning

Smart assistants are a technology that has become very popular today, due to the multiple functions they have, and somehow allow a natural interaction between devices and human beings. The objective is to develop an assistant based on NLP (Natural Language Processing) focused on the Java programming language, with the feature of guiding the user on the use of this language from reliable and validated sources, through speech recognition and speech synthesis by the assistant, allowing communication between the software and the user through natural language, in order to make the time in the consultation of information more efficient since it is considered that from the implementation of the speech to text engine and the subsequent processing of natural language, The search will be carried out on the web obtaining relevant results of precision. In the implemented methodology, the general requirements were defined, such as the voice recognition and identification modules, the search module and the internal storage structure, the design was based on a file-based controller view architecture, implemented through technologies such as PyQt5, Speech Recognition, Pyttsx3, Beautiful Soap among others, developed with the python programming language with the paradigm of object-oriented programming obtaining satisfactory results of precision in the searches by concepts and syntax carried out

Analyzing tourists’ perceptions of air quality is of great significance to the study of tourist experience satisfaction and the image construction of tourism destinations. In this study, using the web crawler technique, we collected 27,500 comments regarding the air quality of 195 of China’s Class 5A tourist destinations posted by tourists on Sina Weibo from January 2011 to December 2017; these comments were then subjected to a content analysis using the Gooseeker, ROST CM (Content Mining System) and BosonNLP (Natural Language Processing) tools. Based on an analysis of the proportions of sentences with different emotional polarities with ROST EA (Emotion Analysis), we measured the sentiment value of texts using the artificial neural network (ANN) machine learning method implemented through a Chinese social media data-oriented Boson platform based on the Python programming language. The content analysis results indicated that in the adaption stage in Sina Weibo, tourists’ perceptions of air quality were mainly positive and had poor air pollution crisis awareness. Objective emotion words exhibited a similarly high proportion as subjective emotion words, indicating that taking both objective and subjective emotion words into account simultaneously helps to comprehensively understand the emotional content of the comments. The sentiment analysis results showed that for the entire text, sentences with positive emotions accounted for 85.53% of the total comments, with a sentiment value of 0.786, which belonged to the positive medium level; the direction of the temporal “up-down-up” changes and the spatial pattern of high in the south and low in the north (while having little difference between the east and the west) were basically consistent with reality. A further exploration of the theoretical basis of the semi-supervised ANN approach or the introduction of other machine learning methods using different data sources will help to analyze this phenomenon in greater depth. The paper provides evidence for new data and methods for air quality research in tourist destinations and provides a new tool for air quality monitoring.

The research aims to build a functioning experimental model of a relational database for operating with a balanced linguistic corpus of a fiction work. Scientific novelty lies in the fact that for the first time within the framework of a humanities study, a database of a linguistic corpus is being modeled with a thorough description and taking into account technical details and based on the provisions of the author’s concept of professionally oriented programming. The work involved three stages: forming a technical task (the structure of two tables of a relational database was developed, the SQLite format was selected, additional columns of the tables were provided for the subsequent expansion of the content of research), writing the source code for creating and filling the database (the Python programming language and the spaCy natural language processing module were used) and testing it based on the material of the texts of three F. Kafka’s novels “The Castle”, “Amerika” and “The Trial” (three functioning databases were created). The research findings have shown that modern natural language processing software tools allow one to create automatically full-fledged databases for processing SQL queries, which can be further expanded manually or automatically.

The aim of the research is to automate the analysis process of the most common grammatical forms and syntactic structures of a foreign or second language to optimize the content and acquisition of English language programs in the training of future Mathematics teachers for professional activities in a multilingual educational environment. The paper theoretically justifies the necessity of a comprehensive frequency analysis of language structures. Software tools for natural language processing (the Python programming language and the spaCy library) were developed, which were subsequently applied in the course “Methods of Mathematical Data Processing” to identify the most frequent grammatical forms and syntactic structures characteristic of the English language of Mathematics. Based on the data obtained, methodological recommendations were formulated for the content and outcomes of English language programs for future Mathematics teachers in a multilingual educational environment. The scientific novelty of the research lies in using digital natural language processing (NLP) tools for a quantitative and statistical analysis of language data, necessary for the development of methodological recommendations for the content and outcomes of foreign/second language programs for subject teacher training. As a result of the research, it was found that digital natural language processing tools can be used to improve the language training of subject teachers in a multilingual educational environment.

Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted significant attention from the software engineering community. Due to its data-driven nature, a DNN model requires massive and high-quality labeled training data to achieve expert-level performance. Collecting such data is often not hard, but the labeling process is notoriously laborious. The task of DNN-based code analysis even worsens the situation because source code labeling also demands sophisticated expertise. Data augmentation has been a popular approach to supplement training data in domains such as computer vision and NLP. However, existing data augmentation approaches in code analysis adopt simple methods, such as data transformation and adversarial example generation, thus bringing limited performance superiority. In this paper, we propose a data augmentation approach MixCode that aims to effectively supplement valid training data, inspired by the recent advance named Mixup in computer vision. Specifically, we first utilize multiple code refactoring methods to generate transformed code that holds consistent labels with the original data. Then, we adapt the Mixup technique to mix the original code with the transformed code to augment the training data. We evaluate MixCode on two programming languages (Java and Python), two code tasks (problem classification and bug detection), four benchmark datasets (JAVA250, Python800, CodRep1, and Refactory), and seven model architectures (including two pretrained models CodeBERT and GraphCodeBERT). Experimental results demonstrate that MixCode outperforms the baseline data augmentation approach by up to 6.24% in accuracy and 26.06% in robustness.

The fundamental aim of this research is to develop a face detection system for a quadcopter in order to follow the face object. This research has two main stages, namely the face detection stage and the position control system. The face detection algorithm used in this research is the haar cascade method which is run using the python and OpenCV programming languages. The algorithm worked well, getting around 16fps on a low spec computer without a GPU unit. The results of the face detection algorithm are proven to be able to recognize faces from the camera installed on the DJI Tello mini drone. The mini drone was chosen because it is small and light, so it is harmless, and testing can be carried out indoors. Besides, the DJI Tello can be programmed easily using the python programming language. The drone’s position is then compared with the set point in the middle of the image to obtain errors so that control signals can be calculated for up/down, forward/backward, and right/left movements. From the testing results, the response speed that occurs in the right/left and up/down movements is less than 2 seconds, while for the forward/backward movement, it is less than 3 seconds.

Phrase embedding is a technique of representing phrases in vector space. A very high effort has been made to develop this technique to improve tasking in various natural language processing (NLP) applications. The evaluation of phrase embedding has been presented in many studies, but most of them focused on the intrinsic or extrinsic evaluation process regardless of the type of the phrase (noun phrases, Verb phrases …). In the literature, there is no study evaluating the embedding of noun phrases, knowing that this type is used by many NLP applications, such as automatic key-phrase extraction (AKE), information retrieval, and question answering. In this article, we will present an empirical study to compare the most common phrase embedding techniques, to determine the most suitable for representing noun phrases. Dataset used in the comparison process consists of the noun phrases from the Inspec and SemEval2010 datasets, to which we have added their manually defined synonyms.