A Context-Dependent Sentiment Analysis of Online Product Reviews based on Dependency Relationships

Consumers often view online consumer product review as a main channel for obtaining product quality information. Existing studies on product review sentiment analysis usually focus on identifying sentiments of individual reviews as a whole, which may not be effective and helpful for consumers when purchase decisions depend on specific features of products. This study proposes a new feature-level sentiment analysis approach for online product reviews. The proposed method uses an extended PageRank algorithm to extract product features and construct expandable context-dependent sentiment lexicons. Moreover, consumers’ sentiment inclinations toward product features expressed in each review can be derived based on term dependency relationships. The empirical evaluation using consumer reviews of two different products shows a higher level of effectiveness of the proposed method for sentiment analysis in comparison to two existing methods. This study provides new research and practical insights on the analysis of online consumer product reviews

Sentiment Analysis and Classification on Amazon Products using Improved Support Vector Machine for Multiclass Classification

There is a huge increase in number of peoples who have been accessing many social networking sites especially user post or reviews for a specific product, company, brand, individual, forums and movies etc. These reviews are helpful in judging customer perception on certain thing. The development of algorithms that could automate the categorization of distinct comments based on feedback from consumers became an analyst project, and this automated classification process is known as sentiment analysis. This research main goal is to analyze Amazon product reviews using an approach to Machine Learning (ML) built around TF-IDF and then employ the Support Vector Machine (SVM) algorithm to categorize the sentiment scores and sentences. SVM can handle binomial classification but the customer reviews is mostly classified into positive, negative and neutral and in some applications, it is fine grained into star ratings such 1-5 or sometimes 1-10. Also, in some applications features or attributes are high in number in which some are irrelevant. Hence, this work applies feature subset algorithm and improves the existing SVM to handle multiclass classification. The Sentiment analysis, Rapidminer tool is considered for classification and the results are visualized, assessed with suitable classification metrics

Aspect-Based Sentiment Analysis of Online Marketplace Reviews Using Convolutional Neural Network

Most online stores provide product review facilities that contain responses to a product. The number of reviews makes it difficult for potential customers to make conclusions, so that sentiment analysis is needed to extract information from these reviews. Most sentiment analysis is done at the document level, so the results were still lacking in detail because the classification is based on the entire sentence or document and does not identify the specific aspect discussed. This research aims to classify aspect-based sentiments from online store reviews using the convolutional neural network (CNN) method with the extraction of features using Word2Vec. The dataset used is Indonesian review data from the site bukalapak.com. The test results on the built system showed that CNN's method of Word2Vec feature extraction has a better score than the naive bayes method with an accuracy value of 85.54%, 96.12% precision, 88.39% recall, and f-measure 92.02%. Classification without using stemming preprocessing on the dataset increases the accuracy by 2.77%

A study of sentiment analysis on customer reviews

The way people shop has changed thanks to the internet and lots of e-commerce like Amazon, Etsy, and Best Buy. In the past, people went to the store and examined products there. Now, people decide on purchasing a product according to its rating and reviews. Sometimes, there is an unfair relationship between a customer's rating and comment. For a book, for instance, although the review is 'the book is so boring and long,' the customer gives a high rating mistakenly or for a specific reason. To reduce this inanity as much as possible and provide a better shopping experience to customers, we should focus on people's thoughts which cannot be done by mistake. In this paper, a sentiment analysis, which examines the opinion or feeling expression, whether positive, negative, or natural, is applied to customer reviews. The reviews are collected by Amazon between 2008 and 2020 in seven different categories for a specific product. The data sets include the product id, name, date, rating, helpfulness, and target. The rating, review, and target would be enough for analysis. The target column represents a positive or negative label based on the ratings, and the reviews are text-based data that is needed to apply preprocessing techniques like whitespace, punctuation, and special character removal. After preprocessing steps, VADER (Valence Aware Dictionary for Sentiment Reasoning) and Textblob, which are lexicon-based sentiment analyzers, are used for properly labeling comments as positive or negative. Since the data sets have more positive-labeled reviews than negative, an oversampling method is applied to balance the dataset. For the feature extraction, the Count Vectorizer and TF- IDF (term frequency-inverse document frequency) are used to create training and test data. Several machine learning algorithms (Logistic Regression, Linear Support Vector Machine, Naive Bayes, Decision Tree, and K-Nearest Neighbors) are used to compare the models and reach the best result.Includes bibliographical references

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

Cross-domain sentiment classification using a sentiment sensitive thesaurus

Automatic classification of sentiment is important for numerous applications such as opinion mining, opinion summarization, contextual advertising, and market analysis. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is costly. Applying a sentiment classifier trained using labeled data for a particular domain to classify sentiment of user reviews on a different domain often results in poor performance. We propose a method to overcome this problem in cross-domain sentiment classification. First, we create a sentiment sensitive distributional thesaurus using labeled data for the source domains and unlabeled data for both source and target domains. Sentiment sensitivity is achieved in the thesaurus by incorporating document level sentiment labels in the context vectors used as the basis for measuring the distributional similarity between words. Next, we use the created thesaurus to expand feature vectors during train and test times in a binary classifier. The proposed method significantly outperforms numerous baselines and returns results that are comparable with previously proposed cross-domain sentiment classification methods. We conduct an extensive empirical analysis of the proposed method on single and multi-source domain adaptation, unsupervised and supervised domain adaptation, and numerous similarity measures for creating the sentiment sensitive thesaurus