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Analysing Effectiveness of Sentiments in Social Media Data Using Machine Learning Techniques

Journal of Computer and Communications 2025年第1期13卷 136-151页

作者： Thambusamy Velmurugan Mohandas Archana Ajith Singh Nongmaithem PG & Research Department of Computer Science D.G.Vaishnav College Chennai India PG Department of IT & BCA D.G.Vaishnav College Chennai India Department of Computer Science Souht East Manipur College Manipur India

Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in different database repositories every day. Most of the review data are useful to new customers for theier further purchases as well as existing companies to view customers feedback about various products. Data Mining and Machine Leaning techniques are familiar to analyse such kind of data to visualise and know the potential use of the purchased items through online. The customers are making quality of products through their sentiments about the purchased items from different online companies. In this research work, it is analysed sentiments of Headphone review data, which is collected from online repositories. For the analysis of Headphone review data, some of the Machine Learning techniques like Support Vector Machines, naive bayes, Decision Trees and Random Forest algorithms and a Hybrid method are applied to find the quality via the customers’ sentiments. The accuracy and performance of the taken algorithms are also analysed based on the three types of sentiments such as positive, negative and neutral.

关键词： Support Vector Machine Random Forest algorithm naive bayes algorithm Machine Learning Techniques Decision Tree algorithm

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An Ensemble-Based Hotel Reviews System Using naive bayes Classifier

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 131-154页

作者： Joseph Bamidele Awotunde Sanjay Misra Vikash Katta Oluwafemi Charles Adebayo Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Computer Science and Communication Otfold University CollegeHaldenNorway

The task of classifying opinions conveyed in any form of text online is referred to as sentiment *** emergence of social media usage and its spread has given room for sentiment analysis in our daily *** media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various *** subject matter can be encountered on social media platforms,such as movie product reviews,consumer opinions,and testimonies,among others,which can be used for sentiment *** rapid uncovering of these web contents contains divergence of many benefits like profit-making,which is one of the most vital of them *** to a recent study,81%of consumers conduct online research prior to making a *** the reviews available online are too huge and numerous for human brains to process and ***,machine learning classifiers are one of the prominent tools used to classify sentiment in order to get valuable information for use in companies like hotels,game companies,and so *** the sentiments of people towards different commodities helps to improve the services for contextual promotions,referral systems,and market ***,this study proposes a sentiment-based framework detection to enable the rapid uncovering of opinionated contents of hotel reviews.A naive bayes classifier was used to process and analyze the dataset for the detection of the polarity of the *** dataset from Datafiniti’s Business Database obtained from Kaggle was used for the experiments in this *** performance evaluation of the model shows a test accuracy of 96.08%,an F1-score of 96.00%,a precision of 96.00%,and a recall of 96.00%.The results were compared with state-of-the-art classifiers and showed a promising performance andmuch better in terms of performancemetrics.

关键词： Sentiment analysis hotel reviews naive bayes algorithm consumer opinions web 2.0 machine learning

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Retrieving reuse component based on semantic

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Journal of Southeast University(English Edition) 2007年第3期23卷 327-332页

作者：王燕陈明赵建辉中国石油大学计算机科学与技术系北京102249

According to the current research status of component retrieval, the component description model based on facet classification is improved by adding semantic features. Furthermore, the component retrieval process model is put forward by combining the domain ontology with the relative concept match algorithm. A detailed illustration of a component reasoning engine and a component classification engine is given and the component classification algorithm is provided by using the naive bayes algorithm based on domain ontology. The experimental results show that the recall ratio and the precision ratio are obviously improved by using the method based on semantics, and demonstrate the feasibility and effectiveness of the proposed method.

关键词： domain ontology facet classification naive bayes algorithm component matching component-based software development

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