• Title/Summary/Keyword: feature-based opinion mining

Search Result 12, Processing Time 0.032 seconds

FEROM: Feature Extraction and Refinement for Opinion Mining

  • Jeong, Ha-Na;Shin, Dong-Wook;Choi, Joong-Min
    • ETRI Journal
    • /
    • v.33 no.5
    • /
    • pp.720-730
    • /
    • 2011
  • Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.

Exploring an Optimal Feature Selection Method for Effective Opinion Mining Tasks

  • Eo, Kyun Sun;Lee, Kun Chang
    • Journal of the Korea Society of Computer and Information
    • /
    • v.24 no.2
    • /
    • pp.171-177
    • /
    • 2019
  • This paper aims to find the most effective feature selection method for the sake of opinion mining tasks. Basically, opinion mining tasks belong to sentiment analysis, which is to categorize opinions of the online texts into positive and negative from a text mining point of view. By using the five product groups dataset such as apparel, books, DVDs, electronics, and kitchen, TF-IDF and Bag-of-Words(BOW) fare calculated to form the product review feature sets. Next, we applied the feature selection methods to see which method reveals most robust results. The results show that the stacking classifier based on those features out of applying Information Gain feature selection method yields best result.

Performance Analysis of Opinion Mining using Word2vec (Word2vec을 이용한 오피니언 마이닝 성과분석 연구)

  • Eo, Kyun Sun;Lee, Kun Chang
    • Proceedings of the Korea Contents Association Conference
    • /
    • 2018.05a
    • /
    • pp.7-8
    • /
    • 2018
  • This study proposes an analysis of the Word2vec-based machine learning classifiers for the sake of opinion mining tasks. As a bench-marking method, BOW (Bag-of-Words) was adopted. On the basis of utilizing the Word2vec and BOW as feature extraction methods, we applied Laptop and Restaurant dataset to LR, DT, SVM, RF classifiers. The results showed that the Word2vec feature extraction yields more improved performance.

  • PDF

Investigating Opinion Mining Performance by Combining Feature Selection Methods with Word Embedding and BOW (Bag-of-Words) (속성선택방법과 워드임베딩 및 BOW (Bag-of-Words)를 결합한 오피니언 마이닝 성과에 관한 연구)

  • Eo, Kyun Sun;Lee, Kun Chang
    • Journal of Digital Convergence
    • /
    • v.17 no.2
    • /
    • pp.163-170
    • /
    • 2019
  • Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

Terms Based Sentiment Classification for Online Review Using Support Vector Machine (Support Vector Machine을 이용한 온라인 리뷰의 용어기반 감성분류모형)

  • Lee, Taewon;Hong, Taeho
    • Information Systems Review
    • /
    • v.17 no.1
    • /
    • pp.49-64
    • /
    • 2015
  • Customer reviews which include subjective opinions for the product or service in online store have been generated rapidly and their influence on customers has become immense due to the widespread usage of SNS. In addition, a number of studies have focused on opinion mining to analyze the positive and negative opinions and get a better solution for customer support and sales. It is very important to select the key terms which reflected the customers' sentiment on the reviews for opinion mining. We proposed a document-level terms-based sentiment classification model by select in the optimal terms with part of speech tag. SVMs (Support vector machines) are utilized to build a predictor for opinion mining and we used the combination of POS tag and four terms extraction methods for the feature selection of SVM. To validate the proposed opinion mining model, we applied it to the customer reviews on Amazon. We eliminated the unmeaning terms known as the stopwords and extracted the useful terms by using part of speech tagging approach after crawling 80,000 reviews. The extracted terms gained from document frequency, TF-IDF, information gain, chi-squared statistic were ranked and 20 ranked terms were used to the feature of SVM model. Our experimental results show that the performance of SVM model with four POS tags is superior to the benchmarked model, which are built by extracting only adjective terms. In addition, the SVM model based on Chi-squared statistic for opinion mining shows the most superior performance among SVM models with 4 different kinds of terms extraction method. Our proposed opinion mining model is expected to improve customer service and gain competitive advantage in online store.

Fuzzy Domain Ontology-based Opinion Mining for Transportation Network Monitoring and City Features Map (교통망 관찰과 도시 특징지도를 위한 퍼지영역 온톨로지 기반 오피니언 마이닝)

  • Ali, Farman;Kwak, Daehan;Islam, SM Riazul;Kim, Kye Hyun;Kwak, Kyung Sup
    • The Journal of The Korea Institute of Intelligent Transport Systems
    • /
    • v.15 no.1
    • /
    • pp.109-118
    • /
    • 2016
  • Traffic congestions are rapidly increasing in urban areas. In order to reduce these problems, it needs real-time data and intelligent techniques to quickly identify traffic activities with useful information. This paper proposes a Fuzzy Domain Ontology(FDO)-based opinion mining system to monitor the transportation network in real-time as well to make a city polarity map for travelers. The proposed system retrieves tweets and reviews related to transportation activities and a city. The feature opinions are extracted from these tweets and reviews and then used FDO to identify transportation and city features polarity. This FDO and intelligent prototype are developed using $Prot{\acute{e}}g{\acute{e}}$ OWL (Web Ontology Language) and JAVA, respectively. The experimental result shows satisfactory improvement in tweets and review's analyzing and opinion mining.

Experimental Study for Effective Combination of Opinion Features (효과적인 의견 자질 결합을 위한 실험적 연구)

  • Han, Kyoung-Soo
    • Journal of the Korean Society for information Management
    • /
    • v.27 no.3
    • /
    • pp.227-239
    • /
    • 2010
  • Opinion retrieval is to retrieve items which are relevant to the user information need topically and include opinion about the topic. This paper aims to find a method to represent user information need for effective opinion retrieval and to analyze the combination methods for opinion features through various experiments. The experiments are carried out in the inference network framework using the Blogs06 collection and 100 TREC test topics. The results show that our suggested representation method based on hidden 'opinion' concept is effective, and the compact model with very small opinion lexicon shows the comparable performance to the previous model on the same test data set.

Feature-Based Summarization Method for a Large Opinion Documents Collection (대용량 오피니언 문서에 대한 특성 기반 요약 기법)

  • Chang, Jae-Young
    • The Journal of the Institute of Internet, Broadcasting and Communication
    • /
    • v.16 no.1
    • /
    • pp.33-42
    • /
    • 2016
  • Recently, an environment in which public opinions are expressed about various areas is expanded around SNSs or internet potals, thus, opinion documents get bigger rapidly. Under these circumstances, it is essential to utilize automatic summarization techniques for understanding whole contents of large opinion documents. However, it is hard to summarize efficiently those documents with traditional text summarization technologies since the documents include subject expressions as well as features of targets objects. Proposed method in this paper defines features of opinion documents, and designed to retrieve representative sentences expressing opinions of those features. In addition, through experiments, we prove the usefulness of proposed method.

Classifying Social Media Users' Stance: Exploring Diverse Feature Sets Using Machine Learning Algorithms

  • Kashif Ayyub;Muhammad Wasif Nisar;Ehsan Ullah Munir;Muhammad Ramzan
    • International Journal of Computer Science & Network Security
    • /
    • v.24 no.2
    • /
    • pp.79-88
    • /
    • 2024
  • The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

Machine Learning Based Stock Price Fluctuation Prediction Models of KOSDAQ-listed Companies Using Online News, Macroeconomic Indicators, Financial Market Indicators, Technical Indicators, and Social Interest Indicators (온라인 뉴스와 거시경제 지표, 금융 지표, 기술적 지표, 관심도 지표를 이용한 코스닥 상장 기업의 기계학습 기반 주가 변동 예측)

  • Kim, Hwa Ryun;Hong, Seung Hye;Hong, Helen
    • Journal of Korea Multimedia Society
    • /
    • v.24 no.3
    • /
    • pp.448-459
    • /
    • 2021
  • In this paper, we propose a method of predicting the next-day stock price fluctuations of 10 KOSDAQ-listed companies in 5G, autonomous driving, and electricity sectors by training SVM, XGBoost, and LightGBM models from macroeconomic·financial market indicators, technical indicators, social interest indicators, and daily positive indices extracted from online news. In the three experiments to find out the usefulness of social interest indicators and daily positive indices, the average accuracy improved when each indicator and index was added to the models. In addition, when feature selection was performed to analyze the superiority of the extracted features, the average importance ranking of the social interest indicator and daily positive index was 5.45 and 1.08, respectively, it showed higher importance than the macroeconomic financial market indicators and technical indicators. With the results of these experiments, we confirmed the effectiveness of the social interest indicators as alternative data and the daily positive index for predicting stock price fluctuation.