Opinion Mining and Analysis of Movie Reviews

Singh, Vibhor; Saxena, Priyansh; Singh, Sadhna; Rajendran, Subash

doi:10.17485/ijst/2017/v10i19/112756

Cited by 13 publications

(4 citation statements)

References 1 publication

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“…A study [5] was conducted to gather data from Twitter and Flexstar, to extract movie ratings from various sources. The data underwent data cleaning, and the text was converted to lowercase using Rapid Miner, Weka, and RStudio to help with data manipulation and comprehension.…”

Section: Related Workmentioning

confidence: 99%

E-Commerce Product Rating Based on Customer Mining for Comments Using Machine Learning Technique

Zaher

2023

ijarcs

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The E-commerce industry heavily relies on customer reviews to gauge product performance. In this paper, we leverage machine-learning techniques to analyze customer comments on a specific product. Our system employs data sets from Kaggle that include popular cell phones from around the world, classifying reviews as positive or negative. This approach helps sellers and company owners selling products online gain insight into customer satisfaction or dissatisfaction with their products. By analyzing customer feedback, businesses can improve their products and boost profits. To test our system, we used the Google Colab environment and experimented with three different algorithms: naïve bias, decision tree, and forest decision tree. The results indicated that the naïve bias algorithm had the highest accuracy (91.3%), precision (95.5%), recall (86.6%), and F-score (91%).

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Section: Related Workmentioning

confidence: 99%

E-Commerce Product Rating Based on Customer Mining for Comments Using Machine Learning Technique

Zaher

2023

ijarcs

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“…Naï ve Bayes is the most suitable for textual classification [32]. In paper [35], The study not only concentrates on the sentiment of reviews but also predicts the rating of the movie using opinion mining algorithms. Different algorithms are used to compare their accuracy.…”

Section: The Algorithms For Opinion Miningmentioning

confidence: 99%

A Comparison of Opinion Mining Algorithms by Using Product Review Data

Sultana¹,

Eva²,

Moon³

et al. 2022

IJIEEB

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After release of Web 2.0 in 2004 user spawned contents on the internet eminently in abundant review sites, online forums, online blogs, and many other sites. Entire user generated contents are considerable bunches of unorganized text written in different languages that encompass user emotions about one or more entities. Mainly predictive analysis exerts the existing data to forecast future outcomes. Currently, a massive amount of researches are being engrossed in the area of opinion mining, also called sentiment analysis, opinion extraction, review analysis, subjective analysis, emotion analysis, and mood extraction. It can be an utmost choice whilst perceiving the meaning and patterns in prevailing data. Most of the time, there are various algorithms available to work with polling. There are contradictory opinions among researchers regarding the effectiveness of algorithms. We have compared different opinion mining algorithms and presented the findings in this paper.

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“…Each review dataset R is viewed by "n-dimensional" attribute vector, X={X 1 , X 2 ... X n }. The probability of each class label negative and positive, P (C positive ) and P (C negative ) can be computed based on the training words and conditional probabilities P (X|C positive ) and P (X|C negative ) and each attribute vector needs to be maximized P (X|C i ) P (C i ) (19) .…”

Section: Naive Bayes (Nb)mentioning

confidence: 99%

Machine Learning Approach to Analyse Ensemble Models and Neural Network Model for E-Commerce Application

Kalaivani¹,

Selvi²

2020

IJST

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Objectives: The main objective of this study is to compare the performance evaluation of ensemble based methods and neural network learning on various combinations of unigram, bigram, and trigram feature vector along with feature selection (IG) and feature reduction (PCA) for sentiment classification of movie reviews. Methods: Bagging and Adaboost are the techniques used in ensemble learning to learn the sentiment classifier to get better classification accuracy, using SVM, NB as a core learner for different models of attribute vectors. The classification results of the ensemble approach are compared with neural network learning for classification of movie reviews. Among the ensemble methods, AdaBoost with base learner SVM outperforms in classifying attribute vectors for model m-iii. The backpropagation algorithm is used to improve classification accuracy in the neural network learning and IG and PCA are used in sentiment classification to reduce the feature length and training time. Findings:The classification results of ensemble based approach are compared with neural network learning. Between the two ensemble based methods, Adaboost + SVM outperform in classifying the sentiment of movie reviews for m-iii feature vector. IG and PCA are used in sentiment classification in order to reduce the feature length. Between the IG and PCA methods, IG performs better than PCA. Among IG+Adaboost+SVM and neural network learning methods, IG+Adaboost+SVM performs better than neural network learning. Improvement: In our application, we are using the ensemble based methods and neural network learning, these methods are compared and analyzed the performance for various levels of feature vectors. A classification algorithm may be designed to analyze the performance with other neural network methods.

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Opinion Mining and Analysis of Movie Reviews

Cited by 13 publications

References 1 publication

E-Commerce Product Rating Based on Customer Mining for Comments Using Machine Learning Technique

E-Commerce Product Rating Based on Customer Mining for Comments Using Machine Learning Technique

A Comparison of Opinion Mining Algorithms by Using Product Review Data

Machine Learning Approach to Analyse Ensemble Models and Neural Network Model for E-Commerce Application

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