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Journal ArticleDOI

Aspect level sentiment analysis using machine learning

01 Nov 2017-Vol. 263, Iss: 4, pp 042009
TL;DR: The proposed model focuses on aspects to produces accurate result by avoiding the spam reviews and uses aspect leveldetection in which features are extracted from the datasets.
Abstract: In modern world the development of web and smartphones increases the usage of online shopping. The overall feedback about product is generated with the help of sentiment analysis using text processing.Opinion mining or sentiment analysis is used to collect and categorized the reviews of product. The proposed system uses aspect leveldetection in which features are extracted from the datasets. The system performs pre-processing operation such as tokenization, part of speech and limitization on the data tofinds meaningful information which is used to detect the polarity level and assigns rating to product. The proposed model focuses on aspects to produces accurate result by avoiding the spam reviews.
Citations
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Journal ArticleDOI
TL;DR: In this paper , the authors presented a model to classify the polarity of the review(s) in Roman Urdu text (reviews) for the purpose of raw data was scraped from the reviews of 20 songs from Indo-Pak Music Industry.
Abstract: Opinion Mining from user reviews is an emerging field. Sentiment Analysis of Natural Language text helps us in finding the opinion of the customers. These reviews can be in any language e.g. English, Chinese, Arabic, Japanese, Urdu, and Hindi. This research presents a model to classify the polarity of the review(s) in Roman Urdu text (reviews). For the purpose, raw data was scraped from the reviews of 20 songs from Indo-Pak Music Industry. In this research a new dataset of 24000 reviews of Roman Urdu text is created. Nine Machine Learning algorithms—Naïve Bayes, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Artificial Neural Networks, Convolutional Neural Network, Recurrent Neural Networks, ID3 and Gradient Boost Tree, are attempted. Logistic Regression outperformed the rest, based on testing and cross validation accuracies that are 92.25% and 91.47% respectively.

6 citations

Journal ArticleDOI
TL;DR: This research presents a model to classify the polarity of the review(s) in Roman Urdu text (reviews) and shows Logistic Regression outperformed the rest, based on testing and cross validation accuracies that are 92.25% and 91.47% respectively.
Abstract: Opinion Mining from user reviews is an emerging field. Sentiment Analysis of Natural Language text helps us in finding the opinion of the customers. These reviews can be in any language e.g. English, Chinese, Arabic, Japanese, Urdu, and Hindi. This research presents a model to classify the polarity of the review(s) in Roman Urdu text (reviews). For the purpose, raw data was scraped from the reviews of 20 songs from Indo-Pak Music Industry. In this research a new dataset of 24000 reviews of Roman Urdu text is created. Nine Machine Learning algorithms—Naïve Bayes, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Artificial Neural Networks, Convolutional Neural Network, Recurrent Neural Networks, ID3 and Gradient Boost Tree, are attempted. Logistic Regression outperformed the rest, based on testing and cross validation accuracies that are 92.25% and 91.47% respectively.

6 citations

Proceedings ArticleDOI
22 Oct 2019
TL;DR: Experimental results indicate that SVM outperforms other classification algorithms, and their combinations with two vectorization approaches have been tested and analyzed.
Abstract: Text classification field of natural language processing has been experiencing remarkable growth in recent years. Especially, sentiment analysis has received a considerable attention from both industry and research community. However, only a few research examples exist for Azerbaijani language. The main objective of this research is to apply various machine learning algorithms for determining the sentiment of news articles in Azerbaijani language. Approximately, 30.000 social news articles have been collected from online news sites and labeled manually as negative or positive according to their sentiment categories. Initially, text preprocessing was implemented to data in order to eliminate the noise. Secondly, to convert text to a more machine-readable form, BOW (bag of words) model has been applied. More specifically, two methodologies of BOW model, which are tf-idf and frequency based model have been used as vectorization methods. Additionally, SVM, Random Forest, and Naive Bayes algorithms have been applied as the classification algorithms, and their combinations with two vectorization approaches have been tested and analyzed. Experimental results indicate that SVM outperforms other classification algorithms.

2 citations

Proceedings ArticleDOI
17 Apr 2023
TL;DR: In this article , the authors proposed different novel deep learning architectures to detect hate speech based on the Arabic context over the Twitter platform by proposing different novel DNN architectures in order to provide a thorough analytical study, which showed the superiority of the performance of the deep learning models over other models in terms of accuracy.
Abstract: Social media is a common medium for expression of views, discussion, sharing of content, and promotion of products and ideas. These views are either polite or obscene. The growth of hate speech is one of these negative aspects and its emergence poses risk factors for societies at various levels. Although there are rules and laws for these platforms, they cannot oversee and control all types of contents. So there is an urgent need to develop modern algorithms to automatically detect hateful content on social media. Arab society is not isolated from the world, and the tremendous usage of social media by its members has highlighted the importance of automated systems that help build an electronic society, free of hate and aggression. This paper aims to detect hate speech based on the Arabic context over the Twitter platform by proposing different novel deep learning architectures in order to provide a thorough analytical study. Also, a comparative study will be presented with different well-known machine learning algorithms, as well as other state-of-the-art algorithms from the literature to be used as a beacon for interested researchers. These models have been applied to the Arabic tweets dataset, which included 15K tweets and 14 features. After training these models, the results obtained for the top two models included an improved Bi-LSTM with an accuracy of 92.20% and a macro F1-score of 92%; a modified CNN with an accuracy of 92.10% and a macro F1-score of 91%. The results also showed the superiority of the performance of the deep learning models over other models in terms of accuracy.

1 citations

References
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Journal ArticleDOI
TL;DR: This paper combines rule-based classification, supervised learning and machine learning into a new combined method, and proposes a semi-automatic, complementary approach in which each classifier can contribute to other classifiers to achieve a good level of effectiveness.

700 citations

Proceedings ArticleDOI
16 Jul 2011
TL;DR: This paper exploits machine learning methods to identify review spam and provides a twoview semi-supervised method, co-training, to exploit the large amount of unlabeled data and shows that the proposed method is effective.
Abstract: In the past few years, sentiment analysis and opinion mining becomes a popular and important task. These studies all assume that their opinion resources are real and trustful. However, they may encounter the faked opinion or opinion spam problem. In this paper, we study this issue in the context of our product review mining system. On product review site, people may write faked reviews, called review spam, to promote their products, or defame their competitors' products. It is important to identify and filter out the review spam. Previous work only focuses on some heuristic rules, such as helpfulness voting, or rating deviation, which limits the performance of this task. In this paper, we exploit machine learning methods to identify review spam. Toward the end, we manually build a spam collection from our crawled reviews. We first analyze the effect of various features in spam identification. We also observe that the review spammer consistently writes spam. This provides us another view to identify review spam: we can identify if the author of the review is spammer. Based on this observation, we provide a twoview semi-supervised method, co-training, to exploit the large amount of unlabeled data. The experiment results show that our proposed method is effective. Our designed machine learning methods achieve significant improvements in comparison to the heuristic baselines.

361 citations

Proceedings ArticleDOI
11 Dec 2011
TL;DR: This work proposes a weakly-supervised approach that utilizes only minimal prior knowledge -- in the form of seed words -- to enforce a direct correspondence between topics and aspects to label sentences with performance that approaches a fully supervised baseline.
Abstract: We investigate the efficacy of topic model based approaches to two multi-aspect sentiment analysis tasks: multi-aspect sentence labeling and multi-aspect rating prediction. For sentence labeling, we propose a weakly-supervised approach that utilizes only minimal prior knowledge -- in the form of seed words -- to enforce a direct correspondence between topics and aspects. This correspondence is used to label sentences with performance that approaches a fully supervised baseline. For multi-aspect rating prediction, we find that overall ratings can be used in conjunction with our sentence labelings to achieve reasonable performance compared to a fully supervised baseline. When gold-standard aspect-ratings are available, we find that topic model based features can be used to improve unsophisticated supervised baseline performance, in agreement with previous multi-aspect rating prediction work. This improvement is diminished, however, when topic model features are paired with a more competitive supervised baseline -- a finding not acknowledged in previous work.

247 citations

Journal ArticleDOI
TL;DR: This research attempts to use the messages of twitter to review a movie by using opinion mining or sentiment analysis to solve the dual optimization problem.

209 citations

Journal ArticleDOI
TL;DR: A sentiment-based rating prediction method (RPS) to improve prediction accuracy in recommender systems and results show the sentiment can well characterize user preferences, which helps to improve the recommendation performance.
Abstract: In recent years, we have witnessed a flourish of review websites It presents a great opportunity to share our viewpoints for various products we purchase However, we face an information overloading problem How to mine valuable information from reviews to understand a user's preferences and make an accurate recommendation is crucial Traditional recommender systems (RS) consider some factors, such as user's purchase records, product category, and geographic location In this work, we propose a sentiment-based rating prediction method (RPS) to improve prediction accuracy in recommender systems Firstly, we propose a social user sentimental measurement approach and calculate each user's sentiment on items/products Secondly, we not only consider a user's own sentimental attributes but also take interpersonal sentimental influence into consideration Then, we consider product reputation, which can be inferred by the sentimental distributions of a user set that reflect customers’ comprehensive evaluation At last, we fuse three factors—user sentiment similarity, interpersonal sentimental influence, and item's reputation similarity—into our recommender system to make an accurate rating prediction We conduct a performance evaluation of the three sentimental factors on a real-world dataset collected from Yelp Our experimental results show the sentiment can well characterize user preferences, which helps to improve the recommendation performance

148 citations