SNIPPET-BASED UNSUPERVISED APPROACH FOR SENTIMENT CLASSIFICATION OF CHINESE ONLINE REVIEWS

2011 ◽  
Vol 10 (06) ◽  
pp. 1097-1110 ◽  
Author(s):  
YIJUN LI ◽  
QIANG YE ◽  
ZIQIONG ZHANG ◽  
TIENAN WANG
Keyword(s):  
Search Engine ◽  
Basic Assumption ◽  
Online Reviews ◽  
Sentiment Classification ◽  
Learning Approaches ◽  
Data Set ◽  
Customer Reviews ◽  
Unsupervised Approach ◽  
Sentiment Orientation

Sentiment classification seeks to identify general attitude of a piece of text of comments or reviews on certain subject, be it positive or negative. Most existing researches on sentiment classification employ supervised learning approaches that rely on annotated data. However, sentiment is expressed differently on different subjects in different domains, and having annotated corpora for every domain of interest is not always practical. This paper proposes an unsupervised learning approach for classifying text of online reviews as recommended or not recommended. The proposed method is based on search engine snippet, summary information on the result page of a search engine. A basic assumption is that terms with similar orientation tend to co-occur. The co-occurrence is measured by utilizing snippets returned from search engines, with a query consisting of the text and a seed positive or negative word. With the information of snippets, the proposed method may estimate the association of candidate terms more accurately. This allows us to reliably predict the sentiment orientation of customer reviews. Texts of customer reviews are then classified as recommended or not recommended if the average sentiment orientations of its phrases are positive or negative. The research data set of this study consists of 600 Chinese online reviews about travel destinations retrieved from Ctrip.com. Our approach achieves an accuracy of 76.5%. Factors that influence the accuracy of the sentiment classification of Chinese online reviews were discussed.

scholarly journals Efficient Multilevel Polarity Sentiment Classification Algorithm using Support Vector Machine and Fuzzy Logic

2019 ◽  
Vol 8 (12) ◽  
pp. 5048-5051
Keyword(s):  
Support Vector Machine ◽  
Social Networking Sites ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Online Reviews ◽  
Sentiment Classification ◽  
Support Vector ◽  
Data Set ◽  
Inference System

This paper discusses an efficient algorithm for sentiment classification of online text reviews posted in social networking sites and blogs which are mostly in unstructured and ungrammatical in nature. Model proposed in this paper utilizes support vector machine supervised learning algorithm and fuzzy inference system for enhancing the degree of sentiment polarity of text reviews and providing multilevel polarity categories. Model is also able to predict degree of sentiment polarity of online reviews. The model accuracy is validated on twitter data set and compared with another earlier model.

Reviews’ length and sentiment as correlates of online reviews’ ratings

2018 ◽  
Vol 28 (3) ◽  
pp. 544-563 ◽  
Author(s):  
Maryam Ghasemaghaei ◽  
Seyed Pouyan Eslami ◽  
Ken Deal ◽  
Khaled Hassanein
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
Marketing Strategies ◽  
Insurance Companies ◽  
Online Review ◽  
Data Set ◽  
Content Type ◽  
Customer Reviews ◽  
Service Companies ◽  
Consumer Review

Purpose The purpose of this paper is twofold: first, to identify and validate reviews’ length and sentiment as correlates of online reviews’ ratings; and second, to understand the emotions embedded in online reviews and how they associate with specific words used in such reviews. Design/methodology/approach A panel data set of customer reviews was collected for auto, life, and home insurance from January 2012 to December 2015 using a web scraping technique. Using a sentiment analysis approach, 1,584 reviews for the auto, home, and life insurance services of 156 insurance companies were analyzed. Findings The results indicate that, since 2013, consumers have generally had more negative emotions than positive ones toward insurance services. The results also show that consumer review sentiment correlates positively and review length correlates negatively with consumer online review ratings. Furthermore, a two-way ANOVA analysis shows that, in general, short reviews with positive sentiment are associated with high review ratings. Practical implications The findings of this study provide service companies, in general, and insurance companies, in particular, with important guidelines that should be considered to increase consumers’ positive attitude toward their services. Originality/value This paper highlights the importance of sentiment analysis in identifying consumer reviews’ emotions and understanding the associations and interactions of reviews’ length and sentiment on online review rating, which can lead to improved marketing strategies.

scholarly journals Aspect Category Detection Based on Attention Mechanism and Bi-Directional LSTM

2019 ◽  
Vol 37 (3) ◽  
pp. 558-564
Author(s):  
Chenchao Zhou ◽  
Qun Chen ◽  
Zhanhuai Li ◽  
Bo Zhao ◽  
Yongjun Xu ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Online Reviews ◽  
Attention Mechanism ◽  
Purchase Decisions ◽  
Short Term ◽  
Data Set ◽  
Part Of Speech ◽  
Urgent Task ◽  
Long Short Term Memory

Online reviews play an increasingly important role in users' purchase decisions. E-commerce websites provide massive user reviews, but it is hard for individuals to make full use of the information. Therefore, it is an urgent task to classify, analyze and summarize the massive comments. In this paper, a model based on attention mechanism and bi-directional long short-term memory (BLSTM) is used to identify the categories of these review objects for the classification of the reviews. The model first uses BLSTM to train the review in the form of word vectors; then according to the part-of-speech, the output vectors of the BLSTM are given corresponding weights. The weights as prior knowledge can guide the learning of attention mechanism to enhance the classification accuracy; finally, the attention mechanism is used to capture category-related important features which are used for category determination. Experiments on the SemEval data set show that our model outperforms the state-of-the-art methods on aspect category detection.

Finding eWOM customers from customer reviews

2019 ◽  
Vol 119 (1) ◽  
pp. 129-147 ◽  
Author(s):  
Pengfei Zhao ◽  
Ji Wu ◽  
Zhongsheng Hua ◽  
Shijian Fang
Keyword(s):  
Evaluation Method ◽  
Online Reviews ◽  
Customer Segmentation ◽  
Product Reviews ◽  
Data Set ◽  
Content Type ◽  
Customer Reviews ◽  
Product Sales ◽  
The Relationship ◽  
New Framework

PurposeThe purpose of this paper is to identify electronic word-of-mouth (eWOM) customers from customer reviews. Thus, firms can precisely leverage eWOM customers to increase their product sales.Design/methodology/approachThis research proposed a framework to analyze the content of consumer-generated product reviews. Specific algorithms were used to identify potential eWOM reviewers, and then an evaluation method was used to validate the relationship between product sales and the eWOM reviewers identified by the authors’ proposed method.FindingsThe results corroborate that online product reviews that are made by the eWOM customers identified by the authors’ proposed method are more related to product sales than customer reviews that are made by non-eWOM customers and that the predictive power of the reviews generated by eWOM customers are significantly higher than the reviews generated by non-eWOM customers.Research limitations/implicationsThe proposed method is useful in the data set, which is based on one type of products. However, for other products, the validity must be tested. Previous eWOM customers may have no significant influence on product sales in the future. Therefore, the proposed method should be tested in the new market environment.Practical implicationsBy combining the method with the previous customer segmentation method, a new framework of customer segmentation is proposed to help firms understand customers’ value specifically.Originality/valueThis study is the first to identify eWOM customers from online reviews and to evaluate the relationship between reviewers and product sales.

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