Sentiment Classification Of Product Reviews Research Articles

E-commerce product review sentiment classification based on a naïve Bayes continuous learning framework

Although statistical learning methods have achieved success in e-commerce platform product review sentiment classification, two problems have limited its practical application: 1) The computational efficiency to process large-scale reviews; 2) the ability to continuously learn from increasing reviews and multiple domains. This paper presents a continuous naïve Bayes learning framework for large-scale and multi-domain e-commerce platform product review sentiment classification. While keeping the high computational efficiency of the traditional naïve Bayes model, we extend the parameter estimation mechanism in naïve Bayes to a continuous learning style. We furthermore propose ways to fine-tune the learned distribution based on three kinds of assumptions to better adapt to different domains. Experimental results on the Amazon product and movie review sentiment datasets show that our model can use the knowledge learned from past domains to guide learning in new domains, and has a better capacity of dealing with reviews that are continuously updated and come from different domains.

High dimensional sentiment classification of product reviews using evolutionary computation

High dimensional sentiment classification of product reviews using evolutionary computation

Deep Learning Framework to Detect the False Analysis of a Product given by Robots and Malicious Users

Product evaluations are precious for upcoming clients in supporting them make choices. To this, numerous mining techniques have been proposed, wherein judging a evaluation sentence’s orientation (e.g. Outstanding or bad) is considered as one of their key worrying conditions. Lately, deep studying has emerged as a powerful technique for fixing sentiment kind issues. A neural network intrinsically learns useful instance routinely without human efforts. But, the fulfilment of deep getting to know pretty is primarily based totally on the supply of big-scale education data. We recommend a unique deep studying framework for product review sentiment classification which employs prevalently to be had rankings as susceptible supervision signs and symptoms. The framework consists of steps: (1) studying a high level representation (an embedding region) which captures the general sentiment distribution of sentences thru score facts; (2) such as a class layer-on top of the embedding layer and use labelled sentences for supervised fine-tuning. We discover styles of low stage community structure for modelling evaluation sentences, specifically, convolution function extractors and prolonged brieftime period memory. To have a take a look at the proposed framework, we gather a data set containing 1.1M weakly classified evaluate sentences and eleven, 754 labelled review sentences from Amazon. Experimental effects display the efficacy of the proposed framework and its superiority over baselines. In this future work todetect false reviews given by robots or by malicious people by taking amount, sometimessome companies may hire people to boost their product ranking higher by assigning fake rating and this malicious people or robots give continuous ranking or review to such product and we can detect such fake rating by analysingratingandremove suchfake rating to give only genuine reviews to users.

Deep Embedding Sentiment Analysis on Product Reviews Using Naive Bayesian Classifier

Product reviews are valuable for upcoming buyers in helping them make decisions. To this end, different opinion mining techniques have been proposed, where judging a review sentence�s orientation (e.g. positive or negative) is one of their key challenges. Recently, deep learning has emerged as an effective means for solving sentiment classification problems. Deep learning is a class of machine learning algorithms that learn in supervised and unsupervised manners. A neural network intrinsically learns a useful representation automatically without human efforts. However, the success of deep learning highly relies on the large-scale training data. We propose a novel deep learning framework for product review sentiment classification which employs prevalently available ratings supervision signals. The framework consists of two steps: (1) learning a high-level representation (an embedding space) which captures the general sentiment distribution of sentences through rating information; (2) adding a category layer on top of the embedding layer and use labelled sentences for supervised fine-tuning. We explore two kinds of low-level network structure for modelling review sentences, namely, convolutional function extractors and long temporary memory. Convolutional layer is the core building block of a CNN and it consists of kernels. Applications are image and video recognition, natural language processing, image classification

High dimensional sentiment classification of product reviews using evolutionary computation

Feature selection is an important process in text classification. In general, traditional feature selection approaches are based on exhaustive search hence become inefficient due to a large search space. Further, this task becomes more challenging as the number of features increases. Recently, evolutionary computation (EC)-based search techniques have received a lot of attention in solving feature selection problem in high-dimensional feature space. This paper proposes a particle swarm optimisation (PSO)-based feature selection approach which is capable of generating the desired number of high-quality features from a large feature space. The proposed algorithm is tested on a large dataset and compared with several existing state-of-the-art algorithms used for feature selection. The accuracy of the underlying classifier has been considered as a measure of performance. Our obtained results demonstrated that the proposed PSO-based feature selection approach outperforms the other traditional feature selection algorithms in all the considered classifiers.

Weakly-Supervised Deep Embedding for Product Review Sentiment Analysis

Product reviews are valuable for upcoming buyers in helping them make decisions. To this end, different opinion mining techniques have been proposed, where judging a review sentence's orientation (e.g., positive or negative) is one of their key challenges. Recently, deep learning has emerged as an effective means for solving sentiment classification problems. A neural network intrinsically learns a useful representation automatically without human efforts. However, the success of deep learning highly relies on the availability of large-scale training data. We propose a novel deep learning framework for product review sentiment classification which employs prevalently available ratings as weak supervision signals. The framework consists of two steps: (1) learning a high level representation (an embedding space) which captures the general sentiment distribution of sentences through rating information; and (2) adding a classification layer on top of the embedding layer and use labeled sentences for supervised fine-tuning. We explore two kinds of low level network structure for modeling review sentences, namely, convolutional feature extractors and long short-term memory. To evaluate the proposed framework, we construct a dataset containing 1.1M weakly labeled review sentences and 11,754 labeled review sentences from Amazon. Experimental results show the efficacy of the proposed framework and its superiority over baselines.

Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus

The literature [-5]contains several reports evaluating the abilities of deep neural networks in text transfer learning. To our knowledge, however, there have been few efforts to fully realize the potential of deep neural networks in cross-domain product review sentiment classification. In this paper, we propose a two-layer convolutional neural network (CNN) for cross-domain product review sentiment classification (LM-CNN-LB). Transfer learning research into product review sentiment classification based on deep neural networks has been limited by the lack of a large-scale corpus; we sought to remedy this problem using a large-scale auxiliary cross-domain dataset collected from Amazon product reviews. Our proposed framework exhibits the dramatic transferability of deep neural networks for cross-domain product review sentiment classification and achieves state-of-the-art performance. The framework also outperforms complex engineered features used with a non-deep neural network method. The experiments demonstrate that introducing large-scale data from similar domains is an effective way to resolve the lack of training data. The LM-CNN-LB trained on the multi-source related domain dataset outperformed the one trained on a single similar domain.

Learning representations from heterogeneous network for sentiment classification of product reviews

There have been increasing interests in natural language processing to explore effective methods in learning better representations of text for sentiment classification in product reviews. However, most existing methods do not consider subtle interplays among words appeared in review text, authors of reviews and products the reviews are associated with. In this paper, we make use of a heterogeneous network to model the shared polarity in product reviews and learn representations of users, products they commented on and words they used simultaneously. The basic idea is to first construct a heterogeneous network which links users, products, words appeared in product reviews, as well as the polarities of the words. Based on the constructed network, representations of nodes are learned using a network embedding method, which are subsequently incorporated into a convolutional neural network for sentiment analysis. Evaluations on the product reviews, including IMDB, Yelp 2013 and Yelp 2014 datasets, show that the proposed approach achieves the state-of-the-art performance.

A topic sentence-based instance transfer method for imbalanced sentiment classification of Chinese product reviews

The increasing interest in sentiment classification of product reviews is due to its potential application for improving e-commerce services and quality of the products. However, in realistic e-commerce environments, the review-related data are imbalanced, and this leads to a problem in which minority class information tends to be ignored during the training phase of a classification model. To address this problem, we propose a topic sentence-based instance transfer method to process imbalanced Chinese product reviews by using an auxiliary dataset (source dataset). The proposed method incorporates a rule and supervised learning hybrid approach to identify a topic sentence of each product review and adds the feature set of the topic sentence to the feature space of sentiment classification. Next, to measure the transferability of instances in source dataset, a greedy algorithm based on information gain of top-N common features is used to select common features. Then, a common feature-based cosine similarity of instances between source dataset and target dataset is introduced to select the transferable instances. Furthermore, a synthetic minority over-sampling technique (Smote) based method is adopted to overcome feature space inconsistency between the source dataset and target dataset. Finally, we immigrate the instances selected in source dataset into target dataset to form a new dataset for the training of classification model. Two datasets collected from Jingdong and Dangdang are the target dataset and source dataset. The experimental results verify that, considering the ability of generalization, our proposed method helps a support vector machine (SVM) to outperform other classification methods, such as the J48, Naive Bayes, Random Forest and Random Committee methods, when applied to datasets produced by resampling and Smote.

Data-driven integration of multiple sentiment dictionaries for lexicon-based sentiment classification of product reviews

In lexicon-based sentiment classification, the problem of contextual polarity must be explicitly handled since it is a major cause for classification error. One way to handle contextual polarity is to revise the prior polarity of the sentiment dictionary to fit the domain. This paper presents a data-driven method of adapting sentiment dictionaries to diverse domains. Our method first merges multiple sentiment dictionaries at the entry word level to expand the dictionary. Then, leveraging the ratio of the positive/negative training data, it selectively removes the entry words that do not contribute to the classification. Finally, it selectively switches the sentiment polarity of the entry words to adapt to the domain. In essence, our method compares the positive/negative review’s dictionary word occurrence ratios with the positive/negative review ratio itself to determine which entry words to be removed and which entry words’ sentiment polarity to be switched. We show that the integrated sentiment dictionary constructed using our ‘merge’, ‘remove’, and ‘switch’ operations robustly outperforms individual dictionaries in the sentiment classification of product reviews across different domains such as smartphones, movies, and books.

Use of negation phrases in automatic sentiment classification of product reviews

Use of negation phrases in automatic sentiment classification of product reviews

Use of negation phrases in automatic sentiment classification of product reviews

This paper reports a study in automatic sentiment classification, i.e., automatically classifying documents as expressing positive or negative sentiments. The study investigates the effectiveness of using a machine-learning algorithm, support vector machine (SVM), on various text features to classify on-line product reviews into recommended (positive sentiment) and not recommended (negative sentiment). In the first part of this study, several approaches, unigrams (individual words), selected words (such as verb, adjective, and adverb), and words labeled with part-of-speech tags were investigated. Using SVM, the unigram approach obtained an accuracy rate of around 76%. Error analysis suggests various approaches for improving classification accuracy: handling of negation phrases, inferencing from superficial words, and handling the problem of comments on parts of the product. The second part of the study investigated the use of negation phrase n-grams to improve classification accuracy. This approach increased the accuracy rate to 79.33%. Compared with traditional subject classification which mainly uses unigrams, syntactic and semantic processing of text appear more important for sentiment classification. We expect that deeper linguistic processing will help increase accuracy for sentiment classification.

Sentiment Classification of Product Reviews Using SVM and Decision Tree Induction

This paper reports a study in automatic sentiment classification, i.e. automatically classifying documents as expressing positive or negative sentiments/opinions. The study investigates the effectiveness of using SVM (Support Vector Machine) and Decision Tree induction on various text features to classify product reviews into recommended (positive sentiment) and not recommended (negative sentiment). Compared with traditional topical classification, it was hypothesized that syntactic and semantic processing of text would be more important for sentiment classification. In this study, five different approaches, unigrams (individual words), part-of-speech tagging, association rules, use of negation, and use of WordNet synsets (identifying a set of synonyms) were investigated. A sample of 1,800 miscellaneous product reviews was retrieved from Review Centre (www.reviewcentre.com. 2003) for the study. 1,200 reviews were used for training, and 600 for validation. Using SVM, the baseline unigrams approach obtained an accuracy rate of 81.3%. The use of WordNet synsets obtained marginally better result of 81.7%. The other text features did not yield better results. Error analysis suggests 3 approaches for improving classification accuracy: making inference from superficial words, solving the problems of comments on parts and negation. Finally, Decision Tree induction was used to generate a list of indicative words that can identify the polarity of articles.