Product Feature Extraction Research Articles

Aspect-based Kano categorization

Customers commonly share opinions and experiences about products via the internet by means of social media and networking sites. The generated textual data is often analysed by means of Sentiment Analysis (SA) as means to assess customer opinions on product features more efficiently than through surveys. To enable a more objective product target setting, the impact of product feature performance changes on customer satisfaction is essential. Kano et al. (1984) presented a survey-based model to classify product features based on their impact on customer satisfaction to aid designers in their product target setting. Approaches extending the Kano model rely on customer surveys as input data. In addition, existing studies classifying extracted product features from textual data (e.g. product reviews) rarely provide a clear separation in terms of Kano categories. Thus, the impact of identified product features on customer satisfaction remains unknown to product designers. This paper presents a methodology for autonomously classifying extracted aspects from textual data into Kano categories. For verification purposes, two examples using coffee machine and smartphone user reviews are presented. Results indicate that the proposed methodology efficiently provides product designers with insightful customer information through the proposed aspect categorization.

Extracting Product Features with NodeRank Algorithm

Extracting Product Features with NodeRank Algorithm

Automated Discovery of Product Feature Inferences Within Large-Scale Implicit Social Media Data

Recently, social media has emerged as an alternative, viable source to extract large-scale, heterogeneous product features in a time and cost-efficient manner. One of the challenges of utilizing social media data to inform product design decisions is the existence of implicit data such as sarcasm, which accounts for 22.75% of social media data, and can potentially create bias in the predictive models that learn from such data sources. For example, if a customer says “I just love waiting all day while this song downloads,” an automated product feature extraction model may incorrectly associate a positive sentiment of “love” to the cell phone's ability to download. While traditional text mining techniques are designed to handle well-formed text where product features are explicitly inferred from the combination of words, these tools would fail to process these social messages that include implicit product feature information. In this paper, we propose a method that enables designers to utilize implicit social media data by translating each implicit message into its equivalent explicit form, using the word concurrence network. A case study of Twitter messages that discuss smartphone features is used to validate the proposed method. The results from the experiment not only show that the proposed method improves the interpretability of implicit messages, but also sheds light on potential applications in the design domains where this work could be extended.

Product Features Extraction for Feature Based Opinion Mining using Latent Dirichlet Allocation

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Predicting Future Importance of Product Features Based on Online Customer Reviews

Previous studies conducted customer surveys based on questionnaires and interviews, and the survey data were then utilized to analyze product features. In recent years, online customer reviews on products became extremely popular, which contain rich information on customer opinions and expectations. However, previous studies failed to properly address the determination of the importance of product features and prediction of their future importance based on online reviews. Accordingly, a methodology for predicting future importance weights of product features based on online customer reviews is proposed in this paper which mainly involves opinion mining, a fuzzy inference method, and a fuzzy time series method. Opinion mining is adopted to analyze the online reviews and extract product features. A fuzzy inference method is used to determine the importance weights of product features using both frequencies and sentiment scores obtained from opinion mining. A fuzzy time series method is adopted to predict the future importance of product features. A case study on electric irons was conducted to illustrate the proposed methodology. To evaluate the effectiveness of the fuzzy time series method in predicting the future importance, the results obtained by the fuzzy time series method are compared with those obtained by the three common forecasting methods. The results of the comparison show that the prediction results based on fuzzy time series method are better than those based on exponential smoothing, simple moving average, and fuzzy moving average methods.

A hybrid method for the extraction and classification of product features from user-generated contents

Abstract – The research we present in this paper focuses on the automatic management of the knowledge about experience goods and services and their features, starting from real texts generated online by internet users. The details about an experiment conducted on a dataset of product reviews, on which we tested a set of rule-based and statistical solutions, will be described in the paper. The main goals are the review classification, the extraction of relevant product features and their systematization into product-driven ontologies. Feature extraction is performed through a rule-based strategy grounded on SentIta , an Italian collection of subjective lexical resources. Features and Reviews are classified thanks to a Distributional Semantic algorithm. In the end, we face the problem of the extracted knowledge organization by integrating the subjective information produced by the internet users within a product-driven ontology. The Natural Language Processing (NLP) tool exploited in the work is LG-Starship , a hybrid framework for Italian texts processing based on the Lexicon-Grammar theory.

Extracting Product Features for Opinion Mining Using Public Conversations in Twitter

The conversational element of Twitter has recently become of particular interest to the marketing community. However, most studies on mining product features through Twitter, have so far employed simple individual tweets rather than considering the whole conversations. In this paper, we empirically evaluate whether employing user interactions in public conversations can improve the product feature extraction from tweets. We propose a conversation-based method which considers a conversation as a reply tree and employs reply links, to effectively extract the product features involved in the messages. We also develop a conversation filtering process which combines scores measured from different aspects including content relevance and social aspects. We conducted our experiments using a manually annotated Twitter corpus involving smartphones and other electronics products. The experimental results show the effectiveness of our proposed method.

A Chinese Product Feature Extraction Method Based on KNN Algorithm

The product feature set of online reviews obtained by the current product feature extraction methods has a low coverage rate of review information. In order to solve this problem, this paper proposes a method of product feature extraction based on KNN algorithm. We establish the classification system of product feature set firstly. Then we extract part of product features as training set manually, and according to similarity between words and the classification system, the product features of all reviews are quickly classified and extracted. At last, the PMI algorithm is used to filter and supplement it to improve the correct rate and the review information coverage rate of product feature set. Through the examples of online clothing reviews data in the Taobao platform, we prove that this method can effectively improve the review information coverage rate of product feature set.

Big data analysis to Features Opinions Extraction of customer

Opinion mining refers to extract subjective information from text data using tools such as natural language processing (NLP), text analysis and computational linguistics. Micro-blogging and social network are the most popular Web 2.0 applications, like Twitter and Facebook which are developed for sharing opinions about different topics. This kind of application becomes a rich data source for opinion mining and sentiment analysis. This information is crucial for managers, who should improve the quality of a product based on customers’ opinions. Concerning the characteristic of a product as mobile phone, it is particularly difficult to identify the features being commented on (e.g., camera quality, battery life, price, etc).In our work, we present a new method that able to extract product features opinions of customer from social networks using text analysis techniques. This task identifies customers opinions regarding product features. We develop a system for retrieving tweets about a product from Twitter and detect product features opinions and their polarity. To validate the effectiveness of this approach, we used a dataset published by Bing Lius group in our approach experimentation. This dataset contains many notated customer reviews of five products such as Canon G3 and Nokia 6610. Next, we test this method with tweets retrieved from Twitter about Nokia, Samsung and Iphone features products.

A Feature-based CAD-LCA Software Integration Approach for Eco-design

LCA is considered as a useful aiding tool for eco-design. However, Major obstacles hindering the employment of LCA to product eco-design include inefficiency in life cycle related data collection and difficulties in interpretation of LCA results into engineer's knowledge field. Though product CAD models are very useful, they cannot be used directly for LCA modeling and analysis, concerning the discrepancy in the principles of the utilized methodological approaches of CAD and LCA. This study aims to propose a CAD-LCA software integration approach based on feature technology. Two types of features, the Product Feature (PF) and Operation Feature (OF), for life cycle representation are proposed first. Then, a NX Unigraphics plug-in tool for PF extraction is developed. Also, a feature-based LCA system prototype tool which has the capabilities of processing product feature models, generating the life cycle process model based on product-to-operation feature mapping and performing LCI and impact assessment is proposed. This tool offers an effective way of life cycle related data integration, LCA result interpretation and application to sustainable product design. A stepped shaft LCA case study is then presented to demonstrate the proposed approach.

Opinion based on Polarity and Clustering for Product Feature Extraction

In recent time, with the rapid development of web 2.0 the number of online user-generated review of product is increases very rapidly. It is very difficult for user to read all reviews and handle all websites to make a valuable decision at feature level. The feature level opinion mining has become very infeasible when people write same feature with contrary words or phrases. To produce a relevant feature based summary of domain synonyms words and phrase, need to be group into same feature group. In this work, we focus on feature based opinion mining and proposed a dynamic system for generate feature based summary of specific feature with specific polarity of opinion according to customer demand on periodic base and changed the summary after a span of period according to customer demand. First a method for feature (frequent and infrequent) extraction using the probabilistic approach at word-level. Second identify the corresponding opinion word and make feature-opinion pair. Third we designed an algorithm for final polarity detection of opinion. Finally, assigning the each feature-opinion pair into the respective feature based cluster (positive, negative or neutral) to generate the summary of specific feature with specific opinion on periodic base which are helpful for user. The experiment results show that our approach can achieves 96%accuracy in feature extraction and 92% accuracy in final polarity detection of feature-opinion pair in feature based summary generation task.

Mining opinionated product features using WordNet lexicographer files

Online customer reviews are an important assessment tool for businesses as they contain feedback that is valuable from the customer perspective. These reviews provide a significant basis on which potential customers can select the product that best meets their preferences. In online reviews, customers describe positive or negative experiences with a product or service or any part of it (i.e. features). Consumers frequently experience difficulty finding the desired product for comparison because of the massive number of online reviews. The automatic extraction of important product features is necessary to support customers in search of relevant product features. These features are the criteria that make it possible for customers to characterise different types of products. This article proposes a domain independent approach for identifying explicit opinionated features and attributes that are strongly related to a specific domain product using lexicographer files in WordNet. In our approach, N_gram analysis and the SentiStrength opinion lexicon have been employed to support the extraction of opinionated features. The empirical evaluation of the proposed system using online reviews of two popular datasets of supervised and unsupervised systems showed that our approach achieved competitive results for feature extraction from product reviews.

RubE: Rule-based methods for extracting product features from online consumer reviews

Motivated by the role of product features in enabling personalized recommendations and marketing, this research aims to extract product features from online consumer reviews. Previous studies are dominated by statistical-based techniques or focused on subjective features that are associated with opinions. In this research, we propose RubE-unsupervised rule-based methods that extract both subjective and objective features from online consumer reviews. We identify objective features by incorporating part–whole relation and review-specific patterns. We extract subjective feature by extending double propagation with indirect dependency and comparative construction. The experiment results demonstrate that RubE significantly outperforms the state-of-the-art techniques for product feature extraction and is generalizable from search goods to experience goods.

Product Features Extraction from Opinions According to Time

Product Features Extraction from Opinions According to Time

Combining local and global information for product feature extraction in opinion documents

Product feature (feature in brief) extraction is one of important tasks in opinion mining as it enables an opinion mining system to provide feature level opinions. Most existing feature extraction methods use only local context information (LCI) in a clause or a sentence (such as co-occurrence or dependency relation) for extraction. But global context information (GCI) is also helpful. In this paper, we propose a combined approach, which integrates LCI and GCI to extract and rank features based on feature score and frequency. Experimental evaluation shows that the combined approach does a good job. It outperforms the baseline extraction methods individually.

A Bayesian Sampling Method for Product Feature Extraction From Large-Scale Textual Data

The authors of this work propose an algorithm that determines optimal search keyword combinations for querying online product data sources in order to minimize identification errors during the product feature extraction process. Data-driven product design methodologies based on acquiring and mining online product-feature-related data are presented with two fundamental challenges: (1) determining optimal search keywords that result in relevant product related data being returned and (2) determining how many search keywords are sufficient to minimize identification errors during the product feature extraction process. These challenges exist because online data, which is primarily textual in nature, may violate several statistical assumptions relating to the independence and identical distribution of samples relating to a query. Existing design methodologies have predetermined search terms that are used to acquire textual data online, which makes the resulting data acquired, a function of the quality of the search term(s) themselves. Furthermore, the lack of independence and identical distribution of text data from online sources impacts the quality of the acquired data. For example, a designer may search for a product feature using the term “screen,” which may return relevant results such as “the screen size is just perfect,” but may also contain irrelevant noise such as “researchers should really screen for this type of error.” A text mining algorithm is introduced to determine the optimal terms without labeled training data that would maximize the veracity of the data acquired to make a valid conclusion. A case study involving real-world smartphones is used to validate the proposed methodology.

Research of Chinese Chunk Parsing in Application of the Product Feature Extraction

Research of Chinese Chunk Parsing in Application of the Product Feature Extraction

Leveraging Large Data with Weak Supervision for Joint Feature and Opinion Word Extraction

Product feature and opinion word extraction is very important for fine granular sentiment analysis. In this paper, we leverage large-scale unlabeled data for joint extraction of feature and opinion words under a knowledge poor setting, in which only a few feature-opinion pairs are utilized as weak supervision. Our major contributions are two-fold: first, we propose a data-driven approach to represent product features and opinion words as a list of corpus-level syntactic relations, which captures rich language structures; second, we build a simple yet robust unsupervised model with prior knowledge incorporated to extract new feature and opinion words, which obtains high performance robustly. The extraction process is based upon a bootstrapping framework which, to some extent, reduces error propagation under large data. Experimental results under various settings compared with state-of-the-art baselines demonstrate that our method is effective and promising.

EXPRS: An extended pagerank method for product feature extraction from online consumer reviews

Online consumer product reviews are a main source for consumers to obtain product information and reduce product uncertainty before making a purchase decision. However, the great volume of product reviews makes it tedious and ineffective for consumers to peruse individual reviews one by one and search for comments on specific product features of their interest. This study proposes a novel method called EXPRS that integrates an extended PageRank algorithm, synonym expansion, and implicit feature inference to extract product features automatically. The empirical evaluation using consumer reviews on three different products shows that EXPRS is more effective than two baseline methods.

Designing a Ranking System for Product Search Engine Based on Mining UGC

Designing a Ranking System for Product Search Engine Based on Mining UGC Dr. Samira Chaabna, Professor Wang Hu, Dr. Mohammed Lutf Abstract With the spread of e-commerce platforms, it becomes extremely difficult for the consumer to choose the right product from a large number of identical products, and different sellers based only on his/her own experience, product pictures or product metadata. Customers reviews present a rich and valuable source of information for potential consumers and manufacturers which can be very helpful in the decision process, but reading all of the available reviews is a hard task and time-consuming. Thus, the automated mining of these reviews and extracting product features in order to generate a raking system present a valuable and useful tool for consumers to make a well-informed decision. In this paper, we propose a ranking system for product search engine based on mining customers reviews. The product features pairs are extracted using Stanford typed dependencies. The evaluation process of the proposed system has passed through two levels: first measuring of accuracy of product feature extraction and classification, and the second one is the determination of the efficiency of the search results and the usability of visual summarization. The results show a high level of accuracy in feature/opinion pairs extraction and high level of participant satisfaction with the ranking and the summarization. Full Text: PDF DOI: 10.15640/jmise.v2n1a2