Stop Words Research Articles

Optimal deep recurrent neural network for sentiment grade classification

Purpose Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the service or product. The Sentiment classification is the process of analyzing the reviews for helping the user to decide whether to purchase the product or not. Design/methodology/approach A rider feedback artificial tree optimization-enabled deep recurrent neural networks (RFATO-enabled deep RNN) is developed for the effective classification of sentiments into various grades. The proposed RFATO algorithm is modeled by integrating the feedback artificial tree (FAT) algorithm in the rider optimization algorithm (ROA), which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of term frequency-inverse document frequency (TF-IDF) features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted. The metrics employed for the evaluation in the proposed RFATO algorithm are accuracy, sensitivity, and specificity. Findings By using the proposed RFATO algorithm, the evaluation metrics such as accuracy, sensitivity and specificity are maximized when compared to the existing algorithms. Originality/value The proposed RFATO algorithm is modeled by integrating the FAT algorithm in the ROA, which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of TF-IDF features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted.

Determination of Motivating Factors of Urban Forest Visitors through Latent Dirichlet Allocation Topic Modeling.

Despite the unique characteristics of urban forests, the motivating factors of urban forest visitors have not been clearly differentiated from other types of the forest resource. This study aims to identify the motivating factors of urban forest visitors, using latent Dirichlet allocation (LDA) topic modeling based on social big data. A total of 57,449 cases of social text data from social blogs containing the keyword “urban forest” were collected from Naver and Daum, the major search engines in South Korea. Then, 17,229 cases were excluded using morpheme analysis and stop word elimination; 40,110 cases were analyzed to identify the motivating factors of urban forest visitors through LDA topic modeling. Seven motivating factors—“Cafe-related Walk”, “Healing Trip”, “Daily Leisure”, “Family Trip”, “Wonderful View”, “Clean Space”, and “Exhibition and Photography”—were extracted; each contained five keywords. This study elucidates the role of forests as a place for healing, leisure, and daily exercise. The results suggest that efforts should be made toward developing various programs regarding the basic functionality of urban forests as a natural resource and a unique place to support a diversity of leisure and cultural activities.

Sentiment Analysis on Covid-19 Vaccination Tweets using Naïve Bayes and LSTM

Social media is one platform where people share their opinions and views on different topics, services, or behaviors that happen around them. Since the COVID19 pandemic that started at the end of 2019, it has been a topic on which people express their sentiments. Recently, the COVID19 vaccination programs have got a lot of responses. In this paper, we have proposed two models: one based on the machine learning approach: Naive Bayes & the other based on deep learning: LSTM, whose goal is to know the sentiment of Asian region tweets towards the vaccine through sentiment analysis. The data were extracted with the help of Twitter API from March 23, 2021, till April 2, 2021. The extraction approach contains keywords with geocoding of some of the Asian countries, especially Nepal, India and Singapore. After collecting data, some preprocessing such as removing numbers, non-English & stop words, removing special characters, and hyperlinks were done. The polarity of tweets was assigned using the Text blob library. The tweets were classified into one of the three: positive, negative, or neutral. Now the data were preprocessed with the splitting of tweets into training & testing sets. Both the models were trained & tested using 10767 unique tweets. This experiment shows that a number of people in these three countries (Nepal, India and Singapore) have positive sentiment towards the vaccine and are taking the first dose of Covid19 vaccine. At last, the accuracy of the LSTM model was found to be 7% greater than that of the Naive Bayes-based model.

Using a sentiment analysis for the examination of tourism blogs – a step by step methodological reflection process

Abstract The article at hand is driven by a methodological interest in the opportunities and challenges of applying an automated text mining approach, particularly a sentiment analysis on various tourism blogs at the same time. The study aims to answer the question to what extent advanced computational methods can improve the data acquisition and analysis of unstructured data sets stemming from various blogs and forums. Furthermore, the authors intend to explore to what extent the sentiment analysis is able to objectify the qualitative results identified by an earlier analysis by the authors using content analysis done by thematic coding. For the purpose of the specific tourism research question in this paper a new approach is proposed, which consists of a combination of sentiment analyses, supervised learning, and dimensionality reduction in order to identify terms that strongly load on specific emotions. The contribution indicates on the one hand, that advanced computational methods have their own specific constraints, but on the other hand, are able to provide a richer and deeper analysis following a quantitative approach. Several issues have to be taken into account, such as data protection constraints, the need for data cleaning, such as word stemming, dimension reduction, such as removal of custom stop words, and the development of descent ontologies. On the other hand, the quantitative method also provides, due to its standardised procedure, a less subjective insight in the given content, but is not less time consuming than traditional content analysis.

PERFORMANCE EVALUATION OF INFORMATION RETRIEVAL SYSTEM USING VECTOR SPACE MODEL: A COMPARATIVE ANALYSIS

The increasing use of the internet has created a vast amount of digital information and it is expanding extremely fast. Therefore, Information retrieval becomes a challenging task to fetch relevant information for users. The aim of this paper was to examine and evaluate the performance of the Information retrieval system through eight experiments to test all the features that can be used in a vector space model. These experiments were compared to show the best and the worst implemented features. The features are represented by applying (tf.idf, stop words, stemming), (tf.idf, No- stop words, stemming), (tf.idf, No- stop words, No-stemming), (tf.idf, stop words, No-stemming), (tf, stop words, stemming), (tf, No- stop words, stemming), (tf, No- stop words, No-stemming), (tf, stop words, No-stemming). Results showed that using stop words, stemming approach, and tf.idf improve the performance of the system. However, when tf was used without using stop words and stemming approaches the performance of the system is declined. In addition, results showed that stop words have a significant effect on the system while the stemming approach has no noticeable effect particularly with tf.

The Effect of Malocclusion on Kurdish Young Children’s Production of Dental, Labio-Dental and Alveolar Sounds: A Case study

The purpose of the present study is to investigate the effect of malocclusion on the production of dental, labio-dental, and alveolar fricative and stop consonants. To achieve this purpose, Fifteen Kurdish young children ranging in age from 8-12 years old were selected to participate in the current study. They were distributed over three groups according to their bite-block occlusions. The participants were asked to pronounce isolate fricative and stop sounds and words which include target sounds, and their voices were recorded. Later, the patricians’ audio-recordings were analyzed via a software praat analysis (wave form and sound spectrograph). The main conclusion of the study was that the children with malocclusions had difficulties in producing the inter-dental, labiodental, and alveolar sounds properly. In addition, distocclusion, open-bite and extra orally deep-bite malocclusions were more significant than other types of malocclusions in affecting target sounds production

Weighted holoentropy-based features with optimised deep belief network for automatic sentiment analysis: reviewing product tweets

ABSTRACT In this paper, a novel sentiment analysis model is implemented, which consists of six stages: (i) Pre-processing, (ii) Keyword extraction and its sentiment categorisation, (iii) Semantic word extraction, (iv) Semantic similarity checking, (v) Feature extraction, and (vi) Classification. Initially, the Mongodb documented tweets are subjected to pre-processing that includes steps such as stop word removal, stemming, and blank space removal. Accordingly, from the pre-processed tweets, the keywords are extracted. Based on the extracted keywords, the prevailing semantic words are extracted after classifying the sentimental keywords. Further, the evaluation of the semantic similarity score with the keywords takes place. Also, it exploits joint holoentropy and cross holoentropy. Here, the extraction of weighted holoentropy features is the main contribution, where a weight function is multiplied by the holoentropy features. To improve the performance of classification, a constant term is used for calculating weight function. It is tuned or optimised in such a way that the accuracy of the proposed method is better. The optimisation strategy uses the hybrid model that merges Particle Swarm Optimisation (PSO) into Whale Optimisation Algorithm (WOA). Hence, the proposed algorithm is named as Swarm Velocity-based WOA (SV-WOA). Finally, the analysis is done to prove the efficiency of the proposed model.

Bag-of-Words Technique in Natural Language Processing: A Primer for Radiologists.

Natural language processing (NLP) is a methodology designed to extract concepts and meaning from human-generated unstructured (free-form) text. It is intended to be implemented by using computer algorithms so that it can be run on a corpus of documents quickly and reliably. To enable machine learning (ML) techniques in NLP, free-form text must be converted to a numerical representation. After several stages of preprocessing including tokenization, removal of stop words, token normalization, and creation of a master dictionary, the bag-of-words (BOW) technique can be used to represent each remaining word as a feature of the document. The preprocessing steps simplify the documents but also potentially degrade meaning. The values of the features in BOW can be modified by using techniques such as term count, term frequency, and term frequency-inverse document frequency. Experience and experimentation will guide decisions on which specific techniques will optimize ML performance. These and other NLP techniques are being applied in radiology. Radiologists' understanding of the strengths and limitations of these techniques will help in communication with data scientists and in implementation for specific tasks. Online supplemental material is available for this article. ©RSNA, 2021.

Social Media and the COVID-19: South African and Zimbabwean Netizens’ Response to a Pandemic

Since the end of 2019, the world faced a major health crisis in the form of the Coronavirus (COVID-19) pandemic. To mitigate the impact of the pandemic, governments across the globe instituted measures such as restricting local and international travel and in many cases, ordering citizens to stay indoors. Considering the social and economic impact of these restrictions it becomes crucial to investigate internet citizens’ (netizens) perception about the precautionary measures adopted. The study is anchored in the digital public sphere theory, which treats social media applications as virtual platforms where netizens commune to share ideas and debate about issues that affect them. Social media platforms already have critical public views on the current pandemic. However, the majority of this data is unstructured and difficult to interpret. Natural language processing (NLP), on the other hand, makes the task of gathering and analysing vast amounts of textual data feasible. Extracting structured knowledge from natural language, however, comes with unique challenges due to diverse linguistic properties including abbreviation, spelling mistakes, punctuations, stop words and non-standard text. In this work, The Latent Dirichlet Allocation (LDA) algorithm was applied to tweeter data to extract topics discussed by netzens from Zimbabwe and South Africa. The primary focus of this paper, is to comparatively explore the variety of topics that occupied twitter communities from the two countries. We examine whether or not the national identities that define and differentiate citizens of these countries also exist on Twitter as evident in the emerging topics. Furthermore, this work investigated public opinion by analysing how citizens discuss the issues around the COVID-19 pandemic on social media

Text mining based sentiment analysis using a novel deep learning approach

Leveraging text mining for sentiment analysis, and integrating text mining and deep learning are the main purposes of this paper. The presented study includes three main steps. At the first step, pre-processing such as tokenization, text cleaning, stop word, stemming, and text normalization has been utilized. Secondly, feature from review and tweets using Bag of Words (BOW) method and Term Frequency $_$Inverse Document Frequency is extracted. Finally, deep learning by dense neural networks is used for classification. This research throws light on understanding the basic concepts of sentiment analysis and then showcases a model which performs deep learning for classification for a movie review and airline$_$ sentiment data set. The performance measure in terms of precision, recall, F1-measure and accuracy were calculated. Based on the results, the proposed method achieved an accuracy of $95.38%$ and $93.84%$ for a movie review and Airline$_$ sentiment, respectively.

Detecting spam e-mails using stop word TF-IDF and stemming algorithm with Naïve Bayes classifier on the multicore GPU

<span>A spam filter is a program which is used to identify unwanted emails and prevents those messages from getting into a user's mail. The study was focused on how the algorithms can be applied on a number of e-mails consisting of both ham and spam e-mails. First, the working principle and steps which are followed for implementation of stop words, TF-IDF and stemming algorithm on NVIDIA’s Tesla P100 GPU are discussed and to verify the findings by executing of Naïve Bayes algorithm. After complete training and testing of the spam e-mails dataset taken from Kaggle by using the proposed method, we got a high training accuracy of 99.67% and got a testing accuracy of about 99.03% on the multicore GPU that boosted the speed of execution of training time period and testing time period which is improved of training and testing accuracy around 0.22% and 0.18% respectively when compared to that after applying only Naïve Bayes i.e. conventional method to the same dataset where we found training and testing accuracy to be 99.45% and 98.85% respectively. Also, we found that training time taken on GPU is 1.361 seconds which was about 1.49X faster than that taken on CPU which is 2.029 seconds. And the testing time taken on GPU is 1.978 seconds which was about 1.15X faster than that taken on CPU which is 2.280 seconds.</span>

The Accuracy Improvement of Text Mining Classification on Hospital Review through The Alteration in The Preprocessing Stage

Sentiment analysis is a part of text mining used to dig up information from a sentence or document. This study focuses on text classification for the purpose of a sentiment analysis on hospital review by customers through criticism and suggestion on Google Maps Review. The data of texts collected still contain a lot of nonstandard words. These nonstandard words cause problem in the preprocessing stage. Thus, the selection and combination of techniques in the preprocessing stage emerge as something crucial for the accuracy improvement in the computation of machine learning. However, not all of the techniques in the preprocessing stage can contribute to improve the accuracy on classification machine. The objective of this study is to improve the accuracy of classification model on hospital review by customers for a sentiment analysis modeling. Through the implementation of the preprocessing technique combination, it can produce a highly accurate classification model. This study experimented with several preprocessing techniques: (1) tokenization, (2) case folding, (3) stop words removal, (4) stemming, and (5) removing punctuation and number. The experiment was done by adding the preprocessing methods: (1) spelling correction and (2) Slang. The result shows that spelling correction and Slang method can assist for improving the accuracy value. Furthermore, the selection of suitable preprocessing technique combination can fasten the training process to produce the more ideal text classification model.

Violence Detection over Online Social Networks: An Arabic Sentiment Analysis Approach

<p class="0abstract">Today, the influence of the social media on different aspects of our lives is increasing, many scholars from various disciplines and majors looking at the social media networks as the ongoing revolution. In Social media networks, many bonds and connections can be established whether being direct or indirect ties. In fact, Social networks are used not only by people but also by companies. People usually create their own profiles and join communities to discuss different common issues that they have interest in. On the other hand, companies also can create their virtual presence on the social media networks to benefit from this media to understand the customers and gather richer information about them. With all of the benefits and advantages of social media networks, they should not always be seen as a safe place for communicating, sharing information and ideas, and establishing virtual communities. These information and ideas could carry with them hatred speeches that must be detected to avoid raising violence. Therefore, web content mining can be used to handle this issue. Web content mining is gaining more concern because of its importance for many businesses and institutions. Sentiment Analysis (SA) is an important sub-area of web content mining. The purpose of SA is to determine the overall sentiment attitude of writer towards a specific entity and classify these opinions automatically. There are two main approaches to build systems of sentiment analysis: the machine learning approach and the lexicon-based approach. This research presents the design and implementation for violence detection over social media using machine learning approach. Our system works on Jordanian Arabic dialect instead of Modern Standard Arabic (MSA). The data was collected from two popular social media websites (Facebook, Twitter) and has used native speakers to annotate the data. Moreover, different preprocessing techniques have been used to show their effect on our model accuracy. The Arabic lexicon was used for generating feature vectors and separate them to features set. Here, we have three well known machine learning algorithms: Support Vector Machine (SVM), Naive Bayes (NB) and k-Nearest Neighbors (KNN). Building on this view, Information Science Research Institute’s (ISRI) stemming and stop word file as a result of preprocessing were used to extract the features. Indeed, several features have been extracted; however, using the SVM classifier reveals that unigram and features extracted from lexicon are characterized by the highest accuracy to detect violence.</p>

Context‐awaretext classification system to improve the quality of text: A detailed investigation and techniques

SummaryText classification is one of the most important tasks to extract information from the Internet and identifying the best text representation settings. With the increase of data volume on the world wide web, the significance of text classification increases. This situation requires huge human efforts to understand and classify the digital data available on the Internet. Text classification is classifying the number of text files into different classes. The data or text available on the Internet is in an unstructured form which increases the difficulty to understand and classify it for useful purposes. This paper proposes a context‐aware text classification system to improve text quality. We use a content‐aware recommendation system to extract the data from well‐known news databases. Text preprocessing techniques like tokenization, stemming, and stop words removal are studied in detail. Furthermore, unigram, bigram, and trigram attributes are also being tested. Attribute selection methods are also examined and their impact on the text classification results. To carry out a detailed investigation, 11 versions are created of each dataset to save the time in experimentation process and applied the different preprocessing techniques to understand the impact of each technique on classification results. The proposed system is compared with the existing approach to check the accuracy where the proposed system achieved better performance.

An automated learning model for sentiment analysis and data classification of Twitter data using balanced CA-SVM

The modern society runs over the social media for their most time of every day. The web users spend their most time in social media and they share many details with their friends. Such information obtained from their chat has been used in several applications. The sentiment analysis is the one which has been applied with Twitter data set toward identifying the emotion of any user and based on those different problems can be solved. Primarily, the data as of the Twitter database is preprocessed. In this step, tokenization, stemming, stop word removal, and number removal are done. The proposed automated learning with CA-SVM based sentiment analysis model reads the Twitter data set. After that they have been processed to extract the features which yield set of terms. Using the terms, the tweets are clustered using TGS-K means clustering which measures Euclidean distance according to different features like semantic sentiment score (SSS), gazetteer and symbolic sentiment support (GSSS), and topical sentiment score (TSS). Further, the method classifies the tweets according to support vector machine (CA-SVM) which classifies the tweet according to the support value which is measured based on the above two measures. The attained results are validated utilizing k-fold cross-validation methodology. Then, the classification is performed by utilizing the Balanced CA-SVM (Deep Learning Modified Neural Network). The results are evaluated and compared with the existing works. The Proposed model achieved 92.48 % accuracy and 92.05% sentiment score contrasted with the existing works.

Stock Market Prediction Using Twitter Sentiment Analysis

Stock market prediction is an important topic in ?nancial engineering especially since new techniques and approaches on this matter are gaining value constantly. In this project, we investigate the impact of sentiment expressed through Twitter tweets on stock price prediction. Twitter is the social media platform which provides a free platform for each individual to express their thoughts publicly. Specifically, we fetch the live twitter tweets of the particular company using the API. All the stop words, special characters are extracted from the dataset. The filtered data is used for sentiment analysis using Naïve bayes classifier. Thus, the tweets are classified into positive, negative and neutral tweets. To predict the stock price, the stock dataset is fetched from yahoo finance API. The stock data along with the tweets data are given as input to the machine learning model to obtain the result. XGBoost classifier is used as a model to predict the stock market price. The obtained prediction value is compared with the actual stock market value. The effectiveness of the proposed project on stock price prediction is demonstrated through experiments on several companies like Apple, Amazon, Microsoft using live twitter data and daily stock data. The goal of the project is to use historical stock data in conjunction with sentiment analysis of news headlines and Twitter posts, to predict the future price of a stock of interest. The headlines were obtained by scraping the website, FinViz, while tweets were taken using Tweepy. Both were analyzed using the Vader Sentiment Analyzer.

SYSTEM BASED REVIEW ANALYSIS

The present paper relates to a review analysis system. In brief, it is the method or the system which is used for collecting, analyzing, and distributing the system’s positive and negative reviews. This constitutes an acquisition module used for acquiring a sentence entered by the customer, a filtering module used for removing stop words, a pre-processing module used for stemming the filtered sentence, a features extraction module used for tokenizing the stemmed sentence, a training module used for generating a review and user platform for displaying the display. The method of review analysis system constitutes of the steps of acquiring sentence entered from the user, removing stop words, stemming, tokenization of stemmed, training stemmed sentence and generating a review and displaying a generated review.

Event Detection through Lexical Chain Based Semantic Similarity Algorithm

Twitter is a platform where millions of people tend to tweet about new events happening in their lives. Celebrities tweeting about the new product endorsement, Politicians tweeting about their views towards a policy or people, Natural calamities occurring are tweeted instantly. Studying this data can provide us with useful information. In this paper, we have proposed an Event detection using a lexical chain based semantic similarity algorithm, for detecting Events from Twitter streams. Lexical chains have been used to preserve the Lexical cohesion in a text. The Twitter data set was collected using “tweepy” API, then pre-processing was done, steps like tokenization, stop word removal, and stemming is carried out and stored the tweets in a text file. Then lexical chains were built using the tweets in the file. The formation of the key graph, with each node as a lexical chain, was carried out. Then the clustering algorithm ‘SCAN’ was used to cluster the lexical chains, the formed clusters represent the keywords of an event. The last summarization step was carried out for each cluster representing an event.

About Methods for Classifying Hidden Language Concepts in Specialized Texts Involving Pseudoinverse, Clustering and Data Grouping

This paper discusses the problems of analysis of hidden language concepts in scientific texts in the Ukrainian language, using methods of text mining, dimensionality reduction, grouping of features and linear classifiers. A corpus of scientific texts and dictionaries, as well as stop words and affixes, has been formed for processing specialized texts. The resulting texts were analyzed and converted into text frequency-inverse document frequency (TF-IDF) feature representation. In order to process the feature vector, we propose to use methods of dimensionality rteduction of the data, in particular, the algorithm for the synthesis of linear systems and Karunen – Loeve transform and grouping of features: T-stochastic grouping of nearest neighbors (T-SNE). A series of experiments were performed on test examples, in particular, for the determination of informational density in the text and classification by keywords in specialized texts using the method of random samples consensus (RANSAC). A method of classification of hidden language concepts was proposed, making use of clustering methods (K-means). As a result of the experiment, the structure of the classifier of hidden language concepts was obtained in structured texts was obtained, which gained a relatively high recognition accuracy (97 – 99 %) using such linear classification algorithms: decision trees and extreme gradient boost machine. The stability of the proposed method is investigated by using the perturbation of the original data by a variational autoencoder, test runs shown that sparse autocoder reduces the mean square error, but the separation band decreases, which affects the convergence of the classification algorithm. In further research, we propose to apply other methods of analysis of structured texts and ways to improve the separability of specialized texts with similar authorial styles and different topic using a proposed set of parameters. Keywords: text processing, language concepts, pseudoinverse, clusterization, methods of data groupings.

Emotion Detection using Social Media and Machine Learning

Today we do a lot of things online from shopping to data sharing on social networking sites. Social networking (SNS) is good for releasing stress and depression by sharing one’s thoughts. Thus, emotion detection has become a hot trend to day. But there is a problem in analyzing emotions on a SNS like twitter as it generates lakhs of tweets each day and it is hard to keep track of the emotion behind each tweet as it is impossible for a human being to read and decide the emotions behind tweets. So, to help understand behind the texts in a SNS site we thought of designing a project which will keep track of the tweets and predict the right emotion behind the tweets whether they have a positive or a negative sentiment behind them. This thought of project can be achieved by a integration of SNS with NLP and machine learning together. For SNS we will use Twitter as it generates a lot of data which is accessible freely using an API. First, we will enter a keyword and fetch tweets from the twitter. Then stop words will be removed from these tweets using NLTK stop words database. Then the tweets will be passed for POS tagging and only right form of grammatical words will be kept and others will be removed. Then we create a training dataset with two types positive and negative. Then SVM algorithm will be trained using this training dataset. Then each tweet will be passed to the SVM as testing dataset which in turn will return classification of each tweet as a whole in two classes positive and negative. Thus, our application will be helpful in recognizing emotion behind a tweet.