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

Zhao, Huiliang; Liu, Zhenghong; Yao, Xuemei; Yang, Qin

doi:10.1016/j.ipm.2021.102656

Cited by 116 publications

(50 citation statements)

References 17 publications

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“…It uses an iterative approach to generate high-dimensional frequent itemsets by low-dimensional frequent itemsets. In this section, after a brief description of the design method of basketball scripting language, the design method of the Apriori algorithm in mining association rules of basketball technical moves is introduced, and the effectiveness of the Apriori algorithm is further demonstrated [ 16 ]. In the basketball technical and tactical analysis system, the improved Apriori algorithm is used to analyze the technical and tactical characteristics of basketball games.…”

Section: Apriori Algorithm For Live Multiattribute Data Mining and Pe...mentioning

confidence: 99%

Live Multiattribute Data Mining and Penalty Decision-Making in Basketball Games Based on the Apriori Algorithm

Zeng

Jia

2022

Applied Bionics and Biomechanics

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The Apriori algorithm is used to conduct an in-depth analysis and research on the relationship between data mining and penalty decision of multiattribute data in the basketball game scene. The technical and tactical features are analyzed using an improved Apriori algorithm for association rule analysis of basketball game data. The algorithm generates association rules based on mining the set of frequent items among basketball technical actions. The improved algorithm can mine the technical moves that are more connected in the game data, and the analysis results are highly instructive. The technical and tactical directed analysis is divided into two parts: technical and tactical directed action analysis and technical and tactical directed cooperation analysis. The key action analysis uses Markov process-based data mining algorithm to analyze the basketball game data for key score transfer steps and key score loss transfer steps. The algorithm can find the key actions of scoring and key actions of conceding points in the game process, and the analysis results can guide basketball training and games, which has high practical value. Using the collated game data as the independent variable and the number of games won and lost as the dependent variable, logistic regression analysis is applied to derive the characteristics that affect winning. Again, the decision tree algorithm is used to select the significant features that affect winning and to make predictions of team performance. Finally, the technical statistics of the main players in the last three seasons are selected, and the association rule algorithm is applied to derive the degree of influence of player performance on the outcome of the game.

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Section: Apriori Algorithm For Live Multiattribute Data Mining and Pe...mentioning

confidence: 99%

Live Multiattribute Data Mining and Penalty Decision-Making in Basketball Games Based on the Apriori Algorithm

Zeng

Jia

2022

Applied Bionics and Biomechanics

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“…In a specific document, a word with a high occurrence rate that is contained in low quantities in other documents in the dataset is considered to reflect the uniqueness of the document according to the TF-IDF algorithm. At present, many studies have been deployed for optimizing term weighting methods based on TF-IDF from different perspectives [9][10][11][12][13].…”

Section: Text Feature Extractionmentioning

confidence: 99%

A Text-Driven Aircraft Fault Diagnosis Model Based on Word2vec and Stacking Ensemble Learning

Zhou

Wei

et al. 2021

Aerospace

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Traditional aircraft maintenance support work is mainly based on structured data. Unstructured data, such as text data, have not been fully used, which means there is a waste of resources. These unstructured data contain a great storehouse of fault knowledge, which could provide decision support for aircraft maintenance support work. Therefore, a text-based fault diagnosis model is proposed in this paper. The proposed method uses Word2vec to map text words into vector space, and the extracted text feature vectors are then input into the classifier based on a stacking ensemble learning scheme. Its performance has been validated using a real aircraft fault text dataset. The results show that the fault diagnosis accuracy of the proposed method is 97.35%, which is about 2% higher than that of the suboptimal method.

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“…Preprocessing is the data preparation stage that aims to simplify the data processing process that works by ignoring unwanted items from the dataset [11]. Preprocessing focuses on data cleaning, such as removing noise in the data, overcoming bad data structures, and missing information.…”

Section: Preprocessing Textmentioning

confidence: 99%

Sentiment Analysis on Work from Home Policy Using Naïve Bayes Method and Particle Swarm Optimization

Arilya

Azhar

Chandranegara

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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At the beginning of 2020, the world was shocked by the coronavirus, which spread rapidly in various countries, one of which was Indonesia. So that the government implemented the Work from Home policy to suppress the spread of Covid-19. This has resulted in many people writing their opinions on the Twitter social media platform and reaping many pros and cons of the community from all aspects. The data source used in this study came from tweets with keywords related to work from home. Several previous studies in this field have not implemented feature selection for sentiment analysis, although the method used is not optimal. So that the contribution in this study is to classify public opinion into positive and negative using sentiment analysis and implement PSO for feature selection and Naïve Bayes for classifiers in building sentiment analysis models. The results showed that the best accuracy was 81% in the classification using Naive Bayes and 86% in the classification using naive Bayes based on PSO through a comparison of 90% training data and 10% test data. With the addition of an accuracy of 5%, it can be concluded that the use of the Particle Swarm Optimization algorithm as a feature selection can help the classification process so that the results obtained are more effective than before.

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

Cited by 116 publications

References 17 publications

Live Multiattribute Data Mining and Penalty Decision-Making in Basketball Games Based on the Apriori Algorithm

Live Multiattribute Data Mining and Penalty Decision-Making in Basketball Games Based on the Apriori Algorithm

A Text-Driven Aircraft Fault Diagnosis Model Based on Word2vec and Stacking Ensemble Learning

Sentiment Analysis on Work from Home Policy Using Naïve Bayes Method and Particle Swarm Optimization

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