Machine Learning-based Sentiment Research Articles

Machine learning-based sentiment analysis of Gujarati reviews

Machine learning-based sentiment analysis of Gujarati reviews

Otellere İlişkin Çevrimiçi Geribildirimlerin Makine Öğrenmesi Yöntemleriyle Duygu Analizi (Sentiment Analysis of Online Feedbacks on Hotels via Machine Learning Methods)

Purpose – Today, one of the ways to quickly understand the consumer is to analyse their feedbacks about the products or services of the business quickly and accurately. In this sense, understanding the sentiment in the feedback with computer-based techniques is one of the ways to be followed. Overlapping level between the sentiments hidden in the feedbacks of hotel customers and their ratings regarding the service they received from the hotel have been examined in this study. Design/methodology/approach – The method used in the research is machine learning-based sentiment analysis. The data set used consists of customer comments on 164 hotels in Antalya, extracted from an online booking site via web scraping method. Compatibility of the comments in the data set with the ratings added to the comments by the customers was tested with a binary sentiment classification via seven different machine learning algorithms including Logistic Regression (LR), Random Forest (RF), CART Decision Tree (CART), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Lineer Discriminant Analysis (LDA), Naive Bayes (NB). Findings – While the average classification success of the algorithms used in supervised sentiment classification was calculated as 81.3%, it was understood that the algorithm produced the most successful results among them was Logistic Regression (87.9%). The methods used in this study were lined up as LR (%87,99), SVM (%86,84), LDA (%86,24), NB (%82,66), RF (%82,00), CART (%76,92) and KNN (%63,91). Discussion – It has been suggested that the model applied in the study, in parallel with the literature, can be used as a tool for hotel managers to make fast, consistent and cost-effective marketing decisions, and that added value can be produced for hotel businesses. It is thought that the study will provide support to both the stakeholders of the accommodation businesses and the researchers who will work on this subject. Moreover, satisfaction with hotel services located in Antalya province instead of international or global tourism satisfaction were investigated in the study. This study can be extended with similar studies for both different minimal locations and larger regions in Turkey. In future studies, it will be possible to realize multilingual applications by using different language libraries. In addition, it is foreseen that the textual expressions can be successfully and quickly resolved in terms of accommodation businesses, as well as cost, time and labour savings.

A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach

Recently, online shopping has turned into a mainstream means for users to purchase as well as consume with the upsurge development of Internet technology. User satisfaction can be improved effectively by doing Sentiment Analysis (SA) of a large quantity of user reviews on e-commerce platforms. It is still challenging to envisage the accurate sentiment polarities of the user reviews because of the changes in sequence length, textual order, along with complicated logic. This paper proposes a new optimized Machine Learning (ML) algorithm called the Local Search Improvised Bat Algorithm based Elman Neural Network (LSIBA-ENN) for the SA of online product reviews. The proposed work of SA encompasses ‘4’ major steps: i) Data Collection (DC), ii) preprocessing, iii) Features Extraction (FE) or Term Weighting (TW), Feature Selection (FS), and polarity or Sentiment Classifications (SC). Initially, the Web Scrapping Tool (WST) is utilized to extract the customer reviews of the products for which the data is gathered as of the E-commerce websites. Next, preprocessing is carried out on the web scrap extracted data. Those preprocessed data go through TW and FS for additional processing by means of Log Term Frequency-based Modified Inverse Class Frequency (LTF-MICF) and Hybrid Mutation based Earth Warm Algorithm (HMEWA). Lastly, the HM-EWA data is rendered to the LSIBA-ENN, which classifies the customer reviews’ sentiment as positive, negative, and neutral. For the performance analysis of the proposed and prevailing classifiers, ‘2’ yardstick datasets are taken. The outcomes exhibit that the LSIBA-ENN attains the best performance in SC when weighted against the existing top-notch algorithms. The observations of the reviewer are exact. The prevailing ENN proffers recall of 87.79 when utilizing the proposed LTF-MICF scheme, whereas ENN only achieve 83.55, 84.03, 85.48, and 86.04 of recall whilst utilizing W2V, TF, TF-IDF, and TF-DFS schemes respectively.

Developing Turkish sentiment analysis models using machine learning and e-commerce data

With the increment of Internet usage, there has been a significant increase in the access and interaction of users in social media, blogs, forums, and criticism sites recently. With social media, access to a large amount of data on various products, services, social and political events is provided. Important feedback about products and services can be obtained as a result of analyzing such data. This study aims to determine the sentiments expressed via texts on social media using machine learning methods. As a result of initial research, it is determined that the best case in which texts and emotions match was the product reviews and ratings used on e-commerce websites. Reviews on different products along with review scores from an e-commerce website have been converted into a table to be used in the machine learning-based sentiment analysis models. Reviews have been classified into three groups as positive, negative, and neutral using the review scores. Considering this claim, Turkish sentiment analysis models were developed using support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and k-nearest neighbors (KNN). Cross-validation results on independent test data taken from the same e-commerce website show that the SVM-based and RF-based sentiment analysis models outperform the other models. In more detail, there is no strict order between SVM-based and RF-based prediction models, but the results of the SVM-based and RF-based models, in general, are the highest or, in the worst case, similar if we compare them with the scores obtained by using the DT-based, LR-based, and KNN-based models. It can be concluded that SVM and RF are viable methods that can be used to classify product reviews into three groups as positive, negative, and neutral within acceptable error rates.

The effects of infotainment on public reaction to North Korea using hybrid text mining: Content analysis, machine learning-based sentiment analysis, and co-word analysis

This study proposes alternative measures of infotainment’s effects on audience perception and reception of news on social media, focusing on infotainment coverage of North Korea. We determine the elements of framing strategies and narrative styles in facilitating public attention, positive and negative responses, and engagement in news content. We used the YouTube application programming interface to collect data from VideoMug, Korea’s most popular YouTube channel, run by the Seoul Broadcasting System. We examined 23,774 replies commenting on North Korea-related video clips from July 1, 2018, to May 17, 2019. The findings show that entertainment and human interest frames were effective in drawing public attention to news coverage about North Korea. Using humor and colloquial language facilitated public attention (both positive and negative) and public engagement. Over half (59.55%) of the comments generated positive emotions; less than one-third generated negative emotions (31.41%); and a few generated neutral ones (9.03%). The infotainment approach helped make South Koreans’ attitudes toward North Korea and inter-Korean relations more positive. A small number of users who served as top authorities were extremely partisan and conducted intense debates about infotainment practices. This study’s hybrid analytical framework using computerized text mining techniques offers both theoretical and methodological insights into the function of infotainment in the context of social media.

Enhancing sentiment classification performance using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights

Machine learning-based sentiment classification is the best-performing method to understand public sentiment. However, the method has some problems, such as noisy features and high-dimensional feature space which affect the sentiment classification performance. To address the problems, this paper proposes a new feature selection using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights. The proposed method was validated using five tweet datasets on different topics both in Indonesian and English, and compared with state-of-the-art of filter and wrapper-based feature selection methods. Experimental results show the proposed method significantly improves sentiment classification performance and decrease computational time.

The mediating effect of media usage on the relationship between anxiety/fear and physician–patient trust during the COVID-19 pandemic

Objective Our study explored whether and how media usage can mediate the path from anxiety and fear to physician–patient trust. Design Study 1 was a population-based, longitudinal study using nationally representative data from 29 provinces in mainland China. The baseline sample (N = 3233) was obtained from February 1 to 9, 2020. Follow-up (N = 1380) took place during March 17 to 24, 2020. Study 2 was a machine learning-based sentiment analysis in which data were captured from Sina Weibo, a Chinese microblogging website, among the most popular official, unofficial, and health-related media accounts. The screened blogs from November to December 2019 and February to March 2020 were scored by Google APIs for positivity and magnitude. Main Outcome Measures Physician-patient trust. Results Study 1 showed fear and anxiety affected changes in physician–patient trust through media usage, the indirect effect of which was 0.14 (0.03) and the 95% CI was [0.08, 0.19]. Study 2 indicated a more positive image of physicians after the outbreak compared to before [F (2, 3537) = 3.646, p = 0.026, partial η2 =0.002]. Conclusion The negative impact of anxiety and fear on physician–patient trust was mediated by media use, which can be explained by the more positive media image during the pandemic.

Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic

One crucial aspect of sentiment analysis is negation handling, where the occurrence of negation can flip the sentiment of a review and negatively affects the machine learning-based sentiment classification. The role of negation in Arabic sentiment analysis has been explored only to a limited extent, especially for colloquial Arabic. In this paper, the authors address the negation problem in colloquial Arabic sentiment classification using the machine learning approach. To this end, they propose a simple rule-based algorithm for handling the problem that affects the performance of a machine learning classifier. The rules were crafted based on observing many cases of negation, simple linguistic knowledge, and sentiment lexicon. They also examine the impact of the proposed algorithm on the performance of different machine learning algorithms. Furthermore, they compare the performance of the classifiers when their algorithm is used against three baselines. The experimental results show that there is a positive impact on the classifiers when the proposed algorithm is used compared to the baselines.

Research on Sentiment Classification of Online Travel Review Text

In recent years, the number of review texts on online travel review sites has increased dramatically, which has provided a novel source of data for travel research. Sentiment analysis is a process that can extract tourists’ sentiments regarding travel destinations from online travel review texts. The results of sentiment analysis form an important basis for tourism decision making. Thus far, there has been minimal concern as to how sentiment analysis methods can be effectively applied to improve the effect of sentiment analysis. However, online travel review texts are largely short texts characterized by uneven sentiment distribution, which makes it difficult to obtain accurate sentiment analysis results. Accordingly, in order to improve the sentiment classification accuracy of online travel review texts, this study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph. Our proposed method extracts keywords from online travel review texts and obtains the concept list of keywords through Microsoft Knowledge Graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. Our proposed method increases the number of classification features used for short text by employing the huge corpus of information associated with the knowledge graph. In addition, this article introduces online travel review text preprocessing, keyword extraction, text representation, sampling, establishment classification labeling, and the selection and application of machine learning-based sentiment classification methods in order to build an effective sentiment classification model for online travel review text. Experiments were implemented and evaluated based on the English review texts of four famous attractions in four countries on the TripAdvisor website. Our experimental results demonstrate that the method proposed in this paper can be used to effectively improve the accuracy of the sentiment classification of online travel review texts. Our research attempts to emphasize and improve the methodological relevance and applicability of sentiment analysis for future travel research.

Spatial Reliability Assessment of Social Media Mining Techniques with Regard to Disaster Domain-Based Filtering

The data generated by social media such as Twitter are classified as big data and the usability of those data can provide a wide range of resources to various study areas including disaster management, tourism, political science, and health. However, apart from the acquisition of the data, the reliability and accuracy when it comes to using it concern scientists in terms of whether or not the use of social media data (SMD) can lead to incorrect and unreliable inferences. There have been many studies on the analyses of SMD in order to investigate their reliability, accuracy, or credibility, but that have not dealt with the filtering techniques applied to with the data before creating the results or after their acquisition. This study provides a methodology for detecting the accuracy and reliability of the filtering techniques for SMD and then a spatial similarity index that analyzes spatial intersections, proximity, and size, and compares them. Finally, we offer a comparison that shows the best combination of filtering techniques and similarity indices to create event maps of SMD by using the Getis-Ord Gi* technique. The steps of this study can be summarized as follows: an investigation of domain-based text filtering techniques for dealing with sentiment lexicons, machine learning-based sentiment analyses on reliability, and developing intermediate codes specific to domain-based studies; then, by using various similarity indices, the determination of the spatial reliability and accuracy of maps of the filtered social media data. The study offers the best combination of filtering, mapping, and spatial accuracy investigation methods for social media data, especially in the case of emergencies, where urgent spatial information is required. As a result, a new similarity index based on the spatial intersection, spatial size, and proximity relationships is introduced to determine the spatial accuracy of the fine-filtered SMD. The motivation for this research is to develop the ability to create an incidence map shortly after a disaster event such as a bombing. However, the proposed methodology can also be used for various domains such as concerts, elections, natural disasters, marketing, etc.

GAWA–A Feature Selection Method for Hybrid Sentiment Classification

Sentiment analysis or opinion mining is the key to natural language processing for the extraction of useful information from the text documents of numerous sources. Several different techniques, i.e., simple rule-based to lexicon-based and more sophisticated machine learning algorithms, have been widely used with different classifiers to get the factual analysis of sentiment. However, lexicon-based sentiment classification is still suffering from low accuracies, mainly due to the deficiency of domain-oriented competitive dictionaries. Similarly, machine learning-based sentiment is also tackling the accuracy constraints because of feature ambiguity from social data. One of the best ways to deal with the accuracy issue is to select the best feature-set and reduce the volume of the feature. This paper proposes a method (namely, GAWA) for feature selection by utilizing the Wrapper Approaches (WA) to select the premier features and the Genetic Algorithm (GA) to reduce the size of the premier features. The novelty of this work is the modified fitness function of heuristic GA to compute the optimal features by reducing the redundancy for better accuracy. This work aims to present a comprehensive model of hybrid sentiment by using the proposed method, GAWA. It will be valued in developing a new approach for the selection of feature-set with a better accuracy level. The experiments revealed that these techniques could reduce the feature-set up-to 61.95% without negotiating the accuracy level. The new optimal feature sets enhanced the efficiency of the Naïve Bayes algorithm up to 92%. This work is compared with the conventional method of feature selection and concluded the 11% better accuracy than PCA and 8% better than PSO. Furthermore, the results are compared with the literature work and found that the proposed method outperformed the previous research.

Emotions identification utilizing periodic handwriting on mobile surfaces

The purpose of this study is to between the learners’ emotional characteristics and styles in touch screen environments. We propose a method to identify the variety of learners' boredom in the learning process utilising handwriting data. The novelty of the method is to avoid explicit polling of learners how do they feel. We use the recurring acquiring of personal handwriting data utilising the computational power of both a mobile device and cloud-based resources. Also, we use machine learning-based sentiment detection in the research. We smoothly inject periodic handwriting tests convolving them with learning objects to study the correlation between learners’ emotions dynamic, they demonstrate, and the ability to focus and think critically. With the help of machine-learning methods and new communication protocols, we can step up the student-centric mobile-based education process by taking advantage of the latest achievements in a big data analysis and cloud computing. Also, we clarify the conceptual model for the testbed used in the experiment. The findings may likely impact the future personalized e-learning systems.

Classification of project management tool reviews using machine learning-based sentiment analysis

Managing the daily responsibilities in an organization is a great task for company administrators. Employing a dedicated project management tool is a great aid in all the phases of project management. With a variety of project management tools available in the market, it is a demanding task to look for a tool appropriate to the needs of the organization. It is an observation by scholars and suggests project management tool users tend to read first the reviews and comments of the product before deciding on the tool they are going to implement. This study facilitates sentiment analysis which can help identify effective and efficient project management tool. Microsoft Office Project is one of the most popular and most reviewed tools for more efficient project and portfolio management. The researchers manually extracted data from various websites containing comments and reviews from different users or reviewers. Machine learning-based-approach using RapidMiner was utilized to analyze the collected data from the web reviews. Sentiment analysis from MS Project reviews was applied using supervised learning K-nearest neighbor (KNN). The first level of classification involved classifying statements as “satisfied” and “dissatisfied.” The second level of classification involved clustering sentiments as cost, experience, task, support and interface. Statements classified as satisfied is greater than dissatisfied. The best features to apply when classifying the PM tool reviews dataset are using stopwords, length, stemming and trigram.

Topic features for machine learning-based sentiment analysis in Indonesian tweets

PurposeThe purpose of this paper is to analyze topics as alternative features for sentiment analysis in Indonesian tweets.Design/methodology/approachGiven Indonesian tweets, the processes of sentiment analysis start by extracting features from the tweets. The features are words or topics. The authors use non-negative matrix factorization to extract the topics and apply a support vector machine to classify the tweets into its sentiment class.FindingsThe authors analyze the accuracy using the two-class and three-class sentiment analysis data sets. Both data sets are about sentiments of candidates for Indonesian presidential election. The experiments show that the standard word features give better accuracies than the topics features for the two-class sentiment analysis. Moreover, the topic features can slightly improve the accuracy of the standard word features. The topic features can also improve the accuracy of the standard word features for the three-class sentiment analysis.Originality/valueThe standard textual data representation for sentiment analysis using machine learning is bag of word and its extensions mainly created by natural language processing. This paper applies topics as novel features for the machine learning-based sentiment analysis in Indonesian tweets.