Aspect-based opinion mining from product reviews using machine learning.

The opinion mining of statements, which is in the extraction of subjective information from product reviews becomes very important due to the development of information technologies. Identifying and evaluating the positivity or negativity of expressions regarding a particular research object allows you to evaluate the success of the advertising campaing, political and economic reforms; to determine consumer attitude to certain products or services. Consequently, the task of aspect-based opinion mining concerning aspects of goods in the evaluation system adapted to the market is relevant and important

Метод межъязыкового аспектно-ориентированного анализа высказываний с использованием машинного обучение категоризационной модели.

Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Today, the Internet has become the largest source of consumer thought. Sentiment analysis and opinion mining is the field of study that analyzes people’s opinions, sentiments, evaluations, attitudes, and emotions from written language. In this paper, we present a study of aspect-based opinion mining using a lexicon-based approach and their adaptation to the processing of responses written in Ukrainian and English. This information helps to build systems to understand customer’s feedback and plan business strategies accordingly. This also helps in predicting the chances of product failure. In this paper, it is explained how machine learning can be used for opinion mining. The research methods used in the work are based on data mining methods, Web mining, machine learning, and information retrieval. The stages of the method of cross-language aspect-oriented analysis of statements are presented. The cross-language categorization of characteristics of goods is considered. The algorithm describes the model learning in cross-language virtual contextual documents.Відгуки про продукцію є головним джерелом інформації для клієнтів і виробників, щоб допомогти їм прийняти відповідні рішення щодо закупівель і виробництва. Сьогодні Інтернет став найбільшим джерелом споживчої думки. Аналіз настроїв і видобування думок є сферою дослідження, яка аналізує думки людей, почуття, оцінки, ставлення та емоції з природно-мовного тексту. У даній роботі представлено дослідження аспектно-орієнтованого видобування думок з використанням лексіконного підходу та його адаптація до обробки відповідей, написаних українською та англійською мовами. Ця інформація допомагає створювати системи для розуміння зворотного зв'язку клієнта та планування відповідних бізнес-стратегій. Це також допомагає прогнозувати шляхи запобігання невдач при просуванні на ринку продуктів. У цій роботі розглянуто використання машинного навчання для видобутку думок клієнтів. Методи дослідження, що використовуються в роботі, базуються на методах інтелектуального аналізу даних, веб-добуванні, машинному навчанні та пошуку інформації. Представлено етапи методу міжмовного аспектно-орієнтованого аналізу тверджень. Розглянуто перехресну категоризацію характеристик товарів. Алгоритм описує модель навчання на міжмовному віртуальному контекстному документі.Отзывы о продукции является главным источником информации для клиентов и производителей, чтобы помочь им принять соответствующие решения в части закупок и производства. Сегодня Интернет стал крупнейшим источником потребительского мнения. Анализ настроений и выявления мыслей является сферой исследования, которая анализирует мнения людей, чувства, оценки, отношения и эмоции с естественно-языкового текста. В данной работе представлено исследование аспектно-ориентированного выявления мыслей с использованием лексиконного подхода и его адаптация к обработки ответов, написанных на украинском и английском языках. Эта информация помогает создавать системы для понимания обратной связи клиента и планирования соответствующих бизнес-стратегий. Это также помогает прогнозировать пути предотвращения неудач при продвижении на рынке продуктов. В этой работе рассмотрено использование машинного обучения для выявления мнений клиентов. Методы исследования, используемые в работе, базируются на методах интеллектуального анализа данных, веб-добывании, машинном обучении и поиска информации. Представлены этапы метода межъязыкового аспектно-ориентированного анализа утверждений. Рассмотрена перекрестная категоризацию характеристик товаров. Алгоритм описывает модель обучения на межъязыковой виртуальном контекстном документе

Efficient Utilization of Dependency Pattern and Sequential Covering for Aspect Extraction Rule Learning

The use of dependency rules for aspect extraction tasks in aspect-based sentiment analysis is a promising approach. One problem with this approach is incomplete rules. This paper presents an aspect extraction rule learning method that combines dependency rules with the Sequential Covering algorithm. Sequential Covering is known for its characteristics in constructing rules that increase positive examples covered and decrease negative ones. This property is vital to make sure that the rule set used has high performance, but not inevitably high coverage, which is a characteristic of the aspect extraction task. To test the new method, four datasets were used from four product domains and three baselines: Double Propagation, Aspectator, and a previous work by the authors. The results show that the proposed approach performed better than the three baseline methods for the F-measure metric, with the highest F-measure value at 0.633

Mining Twitter Sequences of Product Opinions with Multi-Word Aspect Terms

Social media platforms have opened doors to users\u27 opinions and perceptions. The text remains the most popular means of contact on social media, despite different means of communication (audio/video and images). Twitter is one such microblogging platform that allows people to express their thoughts within 280 characters per message. The freedom of expression has made it difficult to understand the polarity (Positive, Negative, or Neutral) of the tweets/posts. Given a corpus of microblog texts (e.g., the new iPhone battery life is good, but camera quality is bad ), mining aspects (e.g., battery life, camera quality) and opinions (e.g., good, bad) of these products are challenging due to the vast data being generated. Aspect-Based Opinion Mining (ABOM) is thus a combination of aspect extraction and opinion mining that allows an enterprise to analyze the data in detail, saving time and money automatically. Existing systems such as Hate Crime Twitter Sentiment (HCTS) and Microblog Aspect Miner (MAM) have been recently proposed to perform ABOM on Twitter. These systems generally go through the four-step approach of obtaining microblog posts, identifying frequent nouns (candidate aspects), pruning the candidate aspects, and getting opinion polarity. However, they differ in how well they prune their candidate features. HCTS uses Apriori based Association rule mining to find the important aspects (single and multi word) of a given product. However, the Apriori based system generate many candidate sequences which generates redundant candidate aspects and HCTS also fails to summarize the category of the aspects (Camera? Battery?). MAM follows the similar approach to that of HCTS for finding the relevant aspects but it further clusters the frequent nouns (aspects) to obtain the relevant aspects. However, it does not identify the multi-word aspects and the aspect category of a product. This thesis proposes a system called Microblog Aspect Sequence Miner (MASM) as an extension of Microblog Aspect Miner (MAM) by replacing the Apriori algorithm with the modified frequent sequential pattern mining algorithm. The system uses the power of sequential pattern mining for aspect extraction in ABOM. The sentiments of the tweets are unknown, so we build our approach in an unsupervised learning manner. The input posts are first classified to identify those tweets which contain the opinion (subjective) to those that do not have any opinion (objective). Then we extract the Parts of Speech tags for the explicit aspects to identify the frequent nouns. The novel frequent pattern mining framework (CM-SPAM) is applied to segment the single and multi-word aspects which generates less sequences as compared to previous approaches. This prior knowledge helps us to operate a topic modeling framework (Latent Dirichlet Allocation) to determine the summary of most common aspects (Aspect Category) and their sentiments for a product. Thefindings demonstrate that the MASM model has a promising performance in finding relevant aspects with reduction of average vector size (cost of candidate/aspect generation) against the MAM and HCTS using the Sanders Twitter corpus dataset. Experimental results with evaluation metrics of execution time, precision, recall, and F-measure indicate that our approach has higher recall and precision than the existing systems

ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS

Aspect-based Opinion Mining (ABOM) systems take as input a corpus about a product and aim to mine the aspects (the features or parts) of the product and obtain the opinions of each aspect (how positive or negative the appraisal or emotions towards the aspect is). A few systems like Twitter Aspect Classifier and Twitter Summarization Framework have been proposed to perform ABOM on microblogs. However, the accuracy of these techniques are easily affected by spam posts and buzzwords. In this thesis we address this problem of removing noisy aspects in ABOM by proposing an algorithm called Microblog Aspect Miner (MAM). MAM classifies the microblog posts into subjective and objective posts, represents the frequent nouns in the subjective posts as vectors, and then clusters them to obtain relevant aspects of the product. MAM achieves a 50% improvement in accuracy in obtaining relevant aspects of products compared to previous systems

Discovering High-Profit Product Feature Groups by mining High Utility Sequential Patterns from Feature-Based Opinions

Extracting a group of features together instead of a single feature from the mined opinions, such as “{battery, camera, design} of a smartphone,” may yield higher profit to the manufactures and higher customer satisfaction, and these can be called High Profit Feature Groups (HPFG). The accuracy of Opinion-Feature Extraction can be improved if more complex sequential patterns of customer reviews are learned and included in the user-behavior analysis to obtain relevant frequent feature groups. Existing Opinion-Feature Extraction systems that use Data Mining techniques with some sequences include those referred to in this thesis as Rashid13OFExt, Rana18OFExt, and HPFG19_HU. Rashid13OFExt and Rana18OFExt systems use Sequential Pattern Mining, Association Rule Mining, and Class Sequential Rules to obtain frequent product features and opinion words from reviews. However, these systems do not discover the frequent high profit features considering utility values (internal and external) such as cost, profit, quantity, or other user preferences. HPFG19_HU system uses High Utility Itemset Mining and Aspect-Based Sentiment Analysis to extract High Utility Aspect groups based on feature-opinion sets. It works on transaction databases of itemsets formed using aspects by considering the high utility values (e.g., are more profitable to the seller?) from the extracted frequent patterns from a set of opinion sentences. However, the HPFG19_HU system does not consider the order of occurrences (sequences) of product features formed in customer opinion sentences that help distinguish similar users and identifying more relevant and related high profit product features. This thesis proposes a system called High Profit Sequential Feature Group based on High Utility Sequences (HPSFG_HUS), which is an extension to the HPFG19_HU system. The proposed system combines Feature-Based Opinion Mining and High Utility Sequential Pattern Mining to extract High Profit Feature Groups from product reviews. The input to the proposed system is the product reviews corpus. The output is the High Profit Sequential Feature Groups in sequence databases that identify sequential patterns in the features extracted from opinions by considering the order of occurrences of features in the review. This method improves on existing system\u27s accuracy in extracting relevant frequent feature groups. The results on retailer’s graphs of extracted High Profit Sequential Feature Groups show that the proposed HPSFG_HUS system provides more accurate high feature groups, sales profit, and user satisfaction. Experimental results evaluating execution time, accuracy, precision, and comparison show higher revenue than the tested existing systems

Aspect-based opinion mining from product reviews using conditional random fields

Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Natural language data is typically very sparse; the most common words are those that do not carry a lot of semantic content, and occurrences of any particular content-bearing word are rare, while co-occurrences of these words are rarer. Mining product aspects, along with corresponding opinions, is essential for Aspect-Based Opinion Mining (ABOM) as a result of the e-commerce revolution. Therefore, the need for automatic mining of reviews has reached a peak. In this work, we deal with ABOM as sequence labelling problem and propose a supervised extraction method to identify product aspects and corresponding opinions. We use Conditional Random Fields (CRFs) to solve the extraction problem and propose a feature function to enhance accuracy. The proposed method is evaluated using two different datasets. We also evaluate the effectiveness of feature function and the optimisation through multiple experiments