Opinion Mining Classification Research Articles

Optimization of opinion mining classification techniques using dragonfly algorithm

<p>With the rapid evolution and growth of the internet, many individuals are using websites, blogs, and social media, and sharing their opinions about any product or service on online social platforms. Opinion mining (OM) is a field of analyzing opinions or reviews given by the public about services or products on online resources into positive, negative, or neutral classes. Governments, businesses, and researchers are using OM to analyze the reviews or opinions of the public. Thus, OM is helping individuals and businesses in better decision making. This paper mainly focuses on the feature extraction, performance analysis of OM classifiers and optimization using swarm intelligence (SI). Our proposed work: i) evaluates the performance of OM classification techniques after data collection, pre-processing, and feature extraction, ii) applies the dragonfly algorithm (DA) for optimization, and iii) evaluates the performance of OM classification techniques after applying DA and compares it with the observed performance of OM classifiers before optimization. The experimental results show that OM classification techniques perform better after optimization using DA in terms of precision, recall, f-score, and accuracy.</p>

Opinion Mining Classification u sing Naive Bayes Algorithm

With the recent advancement in the field of online services, the importance of a review for a product has also gone up. In this paper we focus on the aspect of reducing the time and effort for the user by recommending the best product to him. For this to be achieved, this paper proposes a Naive Bayes Classifier which labels the reviews accurately and combines the reviews to give a final rating to the product. The amazon product review data consisting of both negative and positive reviews was used for training and testing purposes. The model’s performance is evaluated, and results are analysed.

Ontology Based Arabic Opinion Mining

In Arabic language, studies in the area of opinion mining are still limited compared to that being carried out in other languages. In this paper, we highlight the problem for Arabic opinion mining techniques when analysing reviews having different features with different opinion strengths. The traditional works of opinion mining consider all features extracted from the reviews to be equally important, so they fail to determine the correct opinion of the review and make the review's sentiment classification less accurate. This research presents a technique based on an ontology that uses feature level classification to classify Arabic user-generated reviews by identifying the relevant features from the review based on the degree of these features in the ontology tree. Then, we exploit the important features extracted to determine the overall polarity of the review. Moreover, summarisation for each feature is done to determine which feature has satisfied or dissatisfied customers. To evaluate our work, we use public datasets which are hotels and books datasets. We used [Formula: see text]-measure metrics to assess the performance and compare the results with other supervised and unsupervised techniques. Also, subjective evaluation is used in our method to demonstrate the effectiveness of feature and opinion extraction process and summarisation. We show that our method improves the performance compared with other opinion mining classification approaches, obtaining 78.83% [Formula: see text]-measure in hotels domain and 79.18% in books domain. Furthermore, the subjective evaluation shows the effectiveness of our method by getting an average [Formula: see text]-measure of 84.62% in hotels dataset and 86.31% in books dataset.

Ant Colony Optimization Based Feature Selection for Opinion Mining Classification

Ant Colony Optimization Based Feature Selection for Opinion Mining Classification

Framework for Evaluating Feature Selection in Opinion Mining

Opinion mining is important in text mining applications in brand and product positioning, consumer attitude detection, customer relationship management and market research. Applications result in new generation companies, products for reputation management, online market perception and online content monitoring. Web expansion encourages users to contribute or express opinions through blogs, videos and social networking sites which provide information for sentiment analysis regarding a product or service. This study investigates various feature extraction methods performance and opinion mining classification algorithm. Evaluation is through the use of opinions from amazon.com with product reviews. Features extraction is from opinions using Term Document Frequency and Inverse Document Frequency (TDF×IDF). Feature transformation is through Principal Component Analysis (PCA) and kernel PCA. Feature selection techniques like Information Gain (IG), Mutual Information (MI) and Fisher Score select features. Extracted features are classified by Naïve Bayes, k Nearest Neighbour and Classification and Regression Trees (CART) classification algorithms.