Use of Clustering Sentiments for Opinion Mining: An Experimental Analysis

Veeraiah, D.; Rao, Jiahua

doi:10.1007/978-981-13-7166-0_62

Cited by 109 publications

(2 citation statements)

References 1 publication

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“…The reviews, feedback or comments are presenting the emotions and thoughts of the people [46]. The multinational or local companies are interested to analyze the opinion of the customers [47] through sentiment analyses of the reviews.…”

Section: Related Workmentioning

confidence: 99%

Feature Discrimination of News Based on Canopy and KMGC-Search Clustering

et al. 2022

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The internet provides a very vast amount of sources of news and the user has to search for desirable news by spending a lot of time because the user always prefers their related interest, desirable and informative news. The clustering of the news article having a great impact on the preferences of the user. The unsupervised learning techniques such that K-means Clustering and Spectral Clustering are proposed to categorize the news articles by extracting discriminant features that help the user to search and get informative news without wasting time. The BBC news articles dataset is used to perform experiments that consist of 2225 news articles. The TF-IDF feature extraction technique is used with Kmeans clustering and Spectral clustering to get the most similar clusters to categorize the news articles in respective domains. Those domains are sports, tech, entertainment, politics, and business. The clustering algorithms are evaluated using adjusted rand index, V-measure, homogeneity score, completeness score, and Fowlkes mallows score. The experimental results illustrated that K-means clustering performs better than spectral clustering using the TF-IDF feature extraction approach. But to improve the results the canopy centroid selection is used with the grid search optimization technique to optimize the results of the Kmeans and named its as a K-Means using Grid Search based on Canopy (KMGC-Search). The experimental results shows the proposed approach can be used as a viable method for the categorization of news articles.

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Section: Related Workmentioning

confidence: 99%

Feature Discrimination of News Based on Canopy and KMGC-Search Clustering

et al. 2022

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“…Social media network analytics is used to extract the service and the product information of any business and the customer's satisfaction also can be reviewed based on their feedbacks. The major challenge in the social network twitter analysis is how to get the reviews for the products on the concern of features [3].…”

Section: A Literature Reviewmentioning

confidence: 99%

Interrogation of Sentiment Perusing With Hash Counting Vectorizer and Term Inverse Frequency Transformer Using Machine Learning Classification

Rao*,

Devi

2019

IJRTE

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With the fast growing technology, the business is moving towards increasing their profit by interpreting the customer satisfaction. The customer satisfaction can be analyzed in many ways. It is the responsibility of the business to analyze the customer satisfaction in order to improve their turnover and profit. With the current trend, the customers are giving their feedback through mobile and internet. With this overview, this paper attempts to analyze the sentiment of the customer feedback for the movie. The sentiment Analysis on movie Review dataset from the KAGGLE Machine learning repository is used for implementation. The type of sentiment classes is predicted through the following ways. Firstly, the sentiment count for each class is displayed and the top feature words for each sentiment class are also extracted from the dataset. Secondly, the dataset is sampled with counting vectorizer and then fitted with the classifiers like Logistic Regression Classifier, Linear SVM Classifier, Multinomial Naives Bayes Classifier, Gradient Boosting Classifer, Guassian Naive Bayes Classifier, Random Forest Classifier, Decision Tree Classifier and and Extra Tree Classifier. Thirdly, the dataset is sampled with Hashing vectorizer and then fitted with the above specified classifiers. Fourth, the dataset is sampled with TFIFD vectorizer and then fitted with the above specified classifiers. Fifth, the dataset is sampled with TFIFD Transformer and then fitted with the above specified classifiers. Sixth, the Performance analysis of classifiers is performed by analyzing the metrics like Precision, Recall, Fscore and Accuracy. The implementation is carried out using python code in Spyder Anaconda Navigator IP Console. Experimental results shows that the analysis of sentiment done by the random forest classifier is found to be more effective with the Accuracy of 89% for Counting vectorizer and TFIFD transformer, Accuracy of 87% for Hashing vectorizer and Accuracy of 88% for TFIFD vectorizer.

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A Review on Stock Prediction Using Machine Learning Algorithms

Meghwal

Khan

2022

IJSRCSEIT

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The flightiness and unpredictability of the financial exchange render it trying to make a significant benefit utilizing any summed up conspire. This paper means to talk about our AI model, which can create a lot of gain in the US financial exchange by performing live exchanging in the Quantopian stage while utilizing assets liberated from cost. Our top methodology was to utilize outfit learning with four classifiers: Gaussian Naive Bayes, Decision Tree, Logistic Regression with L1 regularization and Stochastic Gradient Descent, to choose whether to go long or short on a specific stock. As indicated by various examinations, stocks produce more noteworthy returns than different resources. Stock returns mostly come from capital increases and profits. Capital increases are the point at which you sell a specific stock at a more exorbitant cost than at which you bought it. Profits are a portion of the benefit that the organization whose stocks you bought makes, and disseminates it to its investors. As indicated by S&P Dow Jones Indices, beginning around 1926, profits have added to 33% of speculation returns while the other 66% have been contributed by capital increases. The possibility of purchasing shares from generally effective organizations like Apple, Amazon, Facebook, Google, and Netflix, together meant by the renowned abbreviation FAANG, during the beginning phases of stock exchanging can appear to be enticing. Financial backers with a high capacity to bear hazard would incline more towards capital additions for acquiring benefit rather than profits. Other people who lean toward a more safe methodology might decide to stay with stocks which have generally been known to give steady and huge profits.

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Use of Clustering Sentiments for Opinion Mining: An Experimental Analysis

Cited by 109 publications

References 1 publication

Feature Discrimination of News Based on Canopy and KMGC-Search Clustering

Feature Discrimination of News Based on Canopy and KMGC-Search Clustering

Interrogation of Sentiment Perusing With Hash Counting Vectorizer and Term Inverse Frequency Transformer Using Machine Learning Classification

A Review on Stock Prediction Using Machine Learning Algorithms

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